Parallel and distributed system projects for java and dot net

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Parallel and distributed system for java / dot net
1.A Stochastic Model to Investigate Data Center
Performance and QoS in IaaS Cloud Computing Systems.
Synopsis:
Cloud data center management is a key problem due to the numerous and heterogeneous
strategies that can be applied, ranging from the VM placement to the federation with other
clouds. Performance evaluation of cloud computing infrastructures is required to predict and
quantify the cost-benefit of a strategy portfolio and the corresponding quality of service (QoS)
experienced by users. Such analyses are not feasible by simulation or on-the-field
experimentation, due to the great number of parameters that have to be investigated. In this
paper, we present an analytical model, based on stochastic reward nets (SRNs), that is both
scalable to model systems composed of thousands of resources and flexible to represent
different policies and cloud-specific strategies. Several performance metrics are defined and
evaluated to analyze the behavior of a cloud data center: utilization, availability, waiting time,
and responsiveness. A resiliency analysis is also provided to take into account load bursts.
Finally, a general approach is presented that, starting from the concept of system capacity, can
help system managers to opportunely set the data center parameters under different working
conditions.
2. A Privacy Leakage Upper Bound Constraint-Based
Approach for Cost-Effective Privacy Preserving of
Intermediate Data Sets in Cloud.
Synopsis:
Cloud computing provides massive computation power and storage capacity which enable
users to deploy computation and data-intensive applications without infrastructure
investment. Along the processing of such applications, a large volume of intermediate data
sets will be generated, and often stored to save the cost of recomputing them. However,
preserving the privacy of intermediate data sets becomes a challenging problem because
adversaries may recover privacy-sensitive information by analyzing multiple intermediate
data sets. Encrypting ALL data sets in cloud is widely adopted in existing approaches to
address this challenge. But we argue that encrypting all intermediate data sets are neither
efficient nor cost-effective because it is very time consuming and costly for data-intensive
applications to en/decrypt data sets frequently while performing any operation on them. In

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this paper, we propose a novel upper bound privacy leakage constraint-based approach to
identify which intermediate data sets need to be encrypted and which do not, so that
privacy-preserving cost can be saved while the privacy requirements of data holders can
still be satisfied. Evaluation results demonstrate that the privacy-preserving cost of
intermediate data sets can be significantly reduced with our approach over existing ones
where all data sets are encrypted.
3. Capacity of Data Collection in Arbitrary Wireless Sensor
Networks.
Synopsis:
Data collection is a fundamental function provided by wireless sensor networks. How to
efficiently collect sensing data from all sensor nodes is critical to the performance of sensor
networks. In this paper, we aim to understand the theoretical limits of data collection in a
TDMA-based sensor network in terms of possible and achievable maximum capacity.
Previously, the study of data collection capacity has concentrated on large-scale random
networks. However, in most of the practical sensor applications, the sensor network is not
uniformly deployed and the number of sensors may not be as huge as in theory. Therefore,
it is necessary to study the capacity of data collection in an arbitrary network. In this paper,
we first derive the upper and lower bounds for data collection capacity in arbitrary networks
under protocol interference and disk graph models. We show that a simple BFS tree-based
method can lead to order-optimal performance for any arbitrary sensor networks. We then
study the capacity bounds of data collection under a general graph model, where two
nearby nodes may be unable to communicate due to barriers or path fading, and discuss
performance implications. Finally, we provide discussions on the design of data collection
under a physical interference model or a Gaussian channel model.
4. A System for Denial-of-Service Attack Detection Based on
Multivariate Correlation Analysis.
Synopsis:
Interconnected systems, such as Web servers, database servers, cloud computing servers
and so on, are now under threads from network attackers. As one of most common and
aggressive means, denial-of-service (DoS) attacks cause serious impact on these
computing systems. In this paper, we present a DoS attack detection system that uses
multivariate correlation analysis (MCA) for accurate network traffic characterization by
extracting the geometrical correlations between network traffic features. Our MCA-based

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DoS attack detection system employs the principle of anomaly based detection in attack
recognition. This makes our solution capable of detecting known and unknown DoS attacks
effectively by learning the patterns of legitimate network traffic only. Furthermore, a triangle-
area-based technique is proposed to enhance and to speed up the process of MCA. The
effectiveness of our proposed detection system is evaluated using KDD Cup 99 data set,
and the influences of both non-normalized data and normalized data on the performance of
the proposed detection system are examined. The results show that our system
outperforms two other previously developed state-of-the-art approaches in terms of
detection accuracy.
5. Dynamic Resource Allocation Using Virtual Machines for
Cloud Computing Environment.
Synopsis:
Cloud computing allows business customers to scale up and down their resource usage
based on needs. Many of the touted gains in the cloud model come from resource
multiplexing through virtualization technology. In this paper, we present a system that uses
virtualization technology to allocate data center resources dynamically based on application
demands and support green computing by optimizing the number of servers in use. We
introduce the concept of "skewness” to measure the unevenness in the multidimensional
resource utilization of a server. By minimizing skewness, we can combine different types of
workloads nicely and improve the overall utilization of server resources. We develop a set of
heuristics that prevent overload in the system effectively while saving energy used. Trace
driven simulation and experiment results demonstrate that our algorithm achieves good
performance.
6. Harnessing the Cloud for Securely Outsourcing Large-
Scale Systems of Linear Equations.
Synopsis:
Cloud computing economically enables customers with limited computational resources to
outsource large-scale computations to the cloud. However, how to protect customers'
confidential data involved in the computations then becomes a major security concern. In
this paper, we present a secure outsourcing mechanism for solving large-scale systems of
linear equations (LE) in cloud. Because applying traditional approaches like Gaussian
elimination or LU decomposition (aka. direct method) to such large-scale LEs would be
prohibitively expensive, we build the secure LE outsourcing mechanism via a completely
different approach-iterative method, which is much easier to implement in practice and only
demands relatively simpler matrix-vector operations. Specifically, our mechanism enables a

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customer to securely harness the cloud for iteratively finding successive approximations to
the LE solution, while keeping both the sensitive input and output of the computation
private. For robust cheating detection, we further explore the algebraic property of matrix-
vector operations and propose an efficient result verification mechanism, which allows the
customer to verify all answers received from previous iterative approximations in one batch
with high probability. Thorough security analysis and prototype experiments on Amazon
EC2 demonstrate the validity and practicality of our proposed design.
7. A Two-Stage Deanonymization Attack against
Anonymized Social Networks.
Synopsis:
Digital traces left by users of online social networking services, even after anonymization,
are susceptible to privacy breaches. This is exacerbated by the increasing overlap in user-
bases among various services. To alert fellow researchers in both the academia and the
industry to the feasibility of such an attack, we propose an algorithm, Seed-and-Grow, to
identify users from an anonymized social graph, based solely on graph structure. The
algorithm first identifies a seed subgraph, either planted by an attacker or divulged by a
collusion of a small group of users, and then grows the seed larger based on the attacker's
existing knowledge of the users' social relations. Our work identifies and relaxes implicit
assumptions taken by previous works, eliminates arbitrary parameters, and improves
identification effectiveness and accuracy. Simulations on real-world collected data sets
verify our claim.
8. Efficient Two-Server Password-Only Authenticated Key
Exchange.
Synopsis:
Password-authenticated key exchange (PAKE) is where a client and a server, who share a
password, authenticate each other and meanwhile establish a cryptographic key by
exchange of messages. In this setting, all the passwords necessary to authenticate clients
are stored in a single server. If the server is compromised, due to, for example, hacking or
even insider attack, passwords stored in the server are all disclosed. In this paper, we
consider a scenario where two servers cooperate to authenticate a client and if one server
is compromised, the attacker still cannot pretend to be the client with the information from
the compromised server. Current solutions for two-server PAKE are either symmetric in the
sense that two peer servers equally contribute to the authentication or asymmetric in the
sense that one server authenticates the client with the help of another server. This paper
presents a symmetric solution for two-server PAKE, where the client can establish different

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cryptographic keys with the two servers, respectively. Our protocol runs in parallel and is
more efficient than existing symmetric two-server PAKE protocol, and even more efficient
than existing asymmetric two-server PAKE protocols in terms of parallel computation.
9. Payments for Outsourced Computations
Synopsis:
With the recent advent of cloud computing, the concept of outsourcing computations,
initiated by volunteer computing efforts, is being revamped. While the two paradigms differ
in several dimensions, they also share challenges, stemming from the lack of trust between
outsourcers and workers. In this work, we propose a unifying trust framework, where correct
participation is financially rewarded: neither participant is trusted, yet outsourced
computations are efficiently verified and validly remunerated. We propose three solutions
for this problem, relying on an offline bank to generate and redeem payments; the bank is
oblivious to interactions between outsourcers and workers. We propose several attacks that
can be launched against our framework and study the effectiveness of our solutions. We
implemented our most secure solution and our experiments show that it is efficient: the
bank can perform hundreds of payment transactions per second and the overheads
imposed on outsourcers and workers are negligible.
10. Application-Aware Local-Global Source Deduplication
for Cloud Backup Services of Personal Storage.
Synopsis:
In personal computing devices that rely on a cloud storage environment for data backup, an
imminent challenge facing source deduplication for cloud backup services is the low
deduplication efficiency due to a combination of the resource-intensive nature of
deduplication and the limited system resources. In this paper, we present ALG-Dedupe, an
Application-aware Local-Global source deduplication scheme that improves data
deduplication efficiency by exploiting application awareness, and further combines local and
global duplicate detection to strike a good balance between cloud storage capacity saving
and deduplication time reduction. We perform experiments via prototype implementation to
demonstrate that our scheme can significantly improve deduplication efficiency over the
state-of-the-art methods with low system overhead, resulting in shortened backup window,
increased power efficiency and reduced cost for cloud backup services of personal storage.

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11. Enabling Dynamic Data and Indirect Mutual Trust for
Cloud Computing Storage Systems.
Synopsis:
Storage-as-a-service offered by cloud service providers (CSPs) is a paid facility that
enables organizations to outsource their sensitive data to be stored on remote servers. In
this paper, we propose a cloud-based storage scheme that allows the data owner to benefit
from the facilities offered by the CSP and enables indirect mutual trust between them. The
proposed scheme has four important features: 1) it allows the owner to outsource sensitive
data to a CSP, and perform full block-level dynamic operations on the outsourced data, i.e.,
block modification, insertion, deletion, and append, 2) it ensures that authorized users (i.e.,
those who have the right to access the owner's file) receive the latest version of the
outsourced data, 3) it enables indirect mutual trust between the owner and the CSP, and 4)
it allows the owner to grant or revoke access to the outsourced data. We discuss the
security issues of the proposed scheme. Besides, we justify its performance through
theoretical analysis and a prototype implementation on Amazon cloud platform to evaluate
storage, communication, and computation overheads.
12. Privacy-Preserving Decentralized Key-Policy Attribute-
Based Encryption.
Synopsis:
Decentralized attribute-based encryption (ABE) is a variant of a multiauthority ABE scheme
where each authority can issue secret keys to the user independently without any
cooperation and a central authority. This is in contrast to the previous constructions, where
multiple authorities must be online and setup the system interactively, which is impractical.
Hence, it is clear that a decentralized ABE scheme eliminates the heavy communication
cost and the need for collaborative computation in the setup stage. Furthermore, every
authority can join or leave the system freely without the necessity of reinitializing the
system. In contemporary multiauthority ABE schemes, a user's secret keys from different
authorities must be tied to his global identifier (GID) to resist the collusion attack. However,
this will compromise the user's privacy. Multiple authorities can collaborate to trace the user
by his GID, collect his attributes, then impersonate him. Therefore, constructing a
decentralized ABE scheme with privacy-preserving remains a challenging research
problem. In this paper, we propose a privacy-preserving decentralized key-policy ABE
scheme where each authority can issue secret keys to a user independently without
knowing anything about his GID. Therefore, even if multiple authorities are corrupted, they
cannot collect the user's attributes by tracing his GID. Notably, our scheme only requires
standard complexity assumptions (e.g., decisional bilinear Diffie-Hellman) and does not

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require any cooperation between the multiple authorities, in contrast to the previous
comparable scheme that requires nonstandard complexity assumptions (e.g., q-decisional
Diffie-Hellman inversion) and interactions among multiple authorities. To the best of our
knowledge, it is the first decentralized ABE scheme with privacy-preserving based on
standard complexity assumptions.
13. Asymmetric Social Proximity Based Private Matching
Protocols for Online Social Networks.
Synopsis:
The explosive growth of Online Social Networks (OSNs) over the past few years has
redefined the way people interact with existing friends and especially make new friends.
Some works propose to let people become friends if they have similar profile attributes.
However, profile matching involves an inherent privacy risk of exposing private profile
information to strangers in the cyberspace. The existing solutions to the problem attempt to
protect users’ privacy by privately computing the intersection or intersection cardinality of
the profile attribute sets of two users. These schemes have some limitations and can still
reveal users’ privacy. In this paper, we leverage community structures to redefine the OSN
model and propose a realistic asymmetric social proximity measure between two users.
Then, based on the proposed asymmetric social proximity, we design three private
matching protocols, which provide different privacy levels and can protect users’ privacy
better than the previous works. We also analyze the computation and communication cost
of these protocols. Finally, we validate our proposed asymmetric proximity measure using
real social network data and conduct extensive simulations to evaluate the performance of
the proposed protocols in terms of computation cost, communication cost, total running
time, and energy consumption. The results show the efficacy of our proposed proximity
measure and better performance of our protocols over the state-of-the-art protocols.
.
14. Error-Tolerant Resource Allocation and Payment
Minimization for Cloud System.
Synopsis:
With virtual machine (VM) technology being increasingly mature, compute resources in
cloud systems can be partitioned in fine granularity and allocated on demand. We make
three contributions in this paper: 1) We formulate a deadline-driven resource allocation
problem based on the cloud environment facilitated with VM resource isolation technology,
and also propose a novel solution with polynomial time, which could minimize users'

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payment in terms of their expected deadlines. 2) By analyzing the upper bound of task
execution length based on the possibly inaccurate workload prediction, we further propose
an error-tolerant method to guarantee task's completion within its deadline. 3) We validate
its effectiveness over a real VM-facilitated cluster environment under different levels of
competition. In our experiment, by tuning algorithmic input deadline based on our derived
bound, task execution length can always be limited within its deadline in the sufficient-
supply situation; the mean execution length still keeps 70 percent as high as user-specified
deadline under the severe competition. Under the original-deadline-based solution, about
52.5 percent of tasks are completed within 0.95-1.0 as high as their deadlines, which still
conforms to the deadline-guaranteed requirement. Only 20 percent of tasks violate
deadlines, yet most (17.5 percent) are still finished within 1.05 times of deadlines.
15. Community home-based multi-copy routing in mobile
social networks.
Synopsis:
A mobile social network (MSN) is a special delay tolerant network (DTN) composed of
mobile nodes with social characteristics. Mobile nodes in MSNs generally visit community
homes frequently, while other locations are visited less frequently. We propose a novel
zero-knowledge MSN routing algorithm, homing spread (HS). The community homes have
a higher priority to spread messages into the network. Theoretical analysis shows that the
proposed algorithm can spread a given number of message copies in an optimal way when
the inter-meeting times between any two nodes and between a node and a community
home follow exponential distributions. We also calculate the expected delivery delay of HS.
In addition, extensive simulations are conducted. Results show that community homes are
important factors in efficient message spreading. By using homes to spread messages
faster, HS achieves a better performance than existing zero-knowledge MSN routing
algorithms, including Epidemic, with a given number of copies, and Spray&Wait.
16. Geocommunity-Based Broadcasting for Data
Dissemination in Mobile Social Networks.
Synopsis:
In this paper, we consider the issue of data broadcasting in mobile social networks (MSNets).
The objective is to broadcast data from a superuser to other users in the network. There are two
main challenges under this paradigm, namely 1) how to represent and characterize user
mobility in realistic MSNets; 2) given the knowledge of regular users' movements, how to design
an efficient superuser route to broadcast data actively. We first explore several realistic data
sets to reveal both geographic and social regularities of human mobility, and further propose the

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concepts of geocommunity and geocentrality into MSNet analysis. Then, we employ a semi-
Markov process to model user mobility based on the geocommunity structure of the network.
Correspondingly, the geocentrality indicating the “dynamic user density” of each geocommunity
can be derived from the semi-Markov model. Finally, considering the geocentrality information,
we provide different route algorithms to cater to the superuser that wants to either minimize total
duration or maximize dissemination ratio. To the best of our knowledge, this work is the first to
study data broadcasting in a realistic MSNet setting. Extensive trace-driven simulations show
that our approach consistently outperforms other existing superuser route design algorithms in
terms of dissemination ratio and energy efficiency.
17. Distributed, Concurrent, and Independent Access to
Encrypted Cloud Databases.
Synopsis:
Placing critical data in the hands of a cloud provider should come with the guarantee of
security and availability for data at rest, in motion, and in use. Several alternatives exist for
storage services, while data confidentiality solutions for the database as a service paradigm
are still immature. We propose a novel architecture that integrates cloud database services
with data confidentiality and the possibility of executing concurrent operations on encrypted
data. This is the first solution supporting geographically distributed clients to connect directly
to an encrypted cloud database, and to execute concurrent and independent operations
including those modifying the database structure. The proposed architecture has the further
advantage of eliminating intermediate proxies that limit the elasticity, availability, and
scalability properties that are intrinsic in cloud-based solutions. The efficacy of the proposed
architecture is evaluated through theoretical analyses and extensive experimental results
based on a prototype implementation subject to the TPC-C standard benchmark for
different numbers of clients and network latencies.
18. Identity-Based Secure DistributedData Storage Schemes.
Synopsis:
Secure distributed data storage can shift the burden of maintaining a large number of files
from the owner to proxy servers. Proxy servers can convert encrypted files for the owner to
encrypted files for the receiver without the necessity of knowing the content of the original
files. In practice, the original files will be removed by the owner for the sake of space
efficiency. Hence, the issues on confidentiality and integrity of the outsourced data must be
addressed carefully. In this paper, we propose two identity-based secure distributed data
storage (IBSDDS) schemes. Our schemes can capture the following properties: (1) The file
owner can decide the access permission independently without the help of the private key
generator (PKG); (2) For one query, a receiver can only access one file, instead of all files

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of the owner; (3) Our schemes are secure against the collusion attacks, namely even if the
receiver can compromise the proxy servers, he cannot obtain the owner's secret key.
Although the first scheme is only secure against the chosen plaintext attacks (CPA), the
second scheme is secure against the chosen ciphertext attacks (CCA). To the best of our
knowledge, it is the first IBSDDS schemes where an access permission is made by the
owner for an exact file and collusion attacks can be protected in the standard model.
19. Expressive, Efficient, and Revocable Data Access Control
for Multi-Authority Cloud Storage.
Synopsis:
Data access control is an effective way to ensure the data security in the cloud. Due to data
outsourcing and untrusted cloud servers, the data access control becomes a challenging
issue in cloud storage systems. Ciphertext-Policy Attribute-based Encryption (CP-ABE) is
regarded as one of the most suitable technologies for data access control in cloud storage,
because it gives data owners more direct control on access policies. However, it is difficult
to directly apply existing CP-ABE schemes to data access control for cloud storage systems
because of the attribute revocation problem. In this paper, we design an expressive,
efficient and revocable data access control scheme for multi-authority cloud storage
systems, where there are multiple authorities co-exist and each authority is able to issue
attributes independently. Specifically, we propose a revocable multi-authority CP-ABE
scheme, and apply it as the underlying techniques to design the data access control
scheme. Our attribute revocation method can efficiently achieve both forward security and
backward security. The analysis and simulation results show that our proposed data access
control scheme is secure in the random oracle model and is more efficient than previous
works.
20. IP-Geolocation Mapping for Moderately Connected
Internet Regions.
Synopsis:
Most IP-geolocation mapping schemes [14], [16], [17], [18] take delay-measurement
approach, based on the assumption of a strong correlation between networking delay and
geographical distance between the targeted client and the landmarks. In this paper,
however, we investigate a large region of moderately connected Internet and find the delay-
distance correlation is weak. But we discover a more probable rule - with high probability
the shortest delay comes from the closest distance. Based on this closest-shortest rule, we
develop a simple and novel IP-geolocation mapping scheme for moderately connected
Internet regions, called GeoGet. In GeoGet, we take a large number of webservers as

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passive landmarks and map a targeted client to the geolocation of the landmark that has the
shortest delay. We further use JavaScript at targeted clients to generate HTTP/Get probing
for delay measurement. To control the measurement cost, we adopt a multistep probing
method to refine the geolocation of a targeted client, finally to city level. The evaluation
results show that when probing about 100 landmarks, GeoGet correctly maps 35.4 percent
clients to city level, which outperforms current schemes such as GeoLim [16] and GeoPing
[14] by 270 and 239 percent, respectively, and the median error distance in GeoGet is
around 120 km, outperforming GeoLim and GeoPing by 37 and 70 percent, respectively.
21. Hop-by-Hop Message Authenticationand Source Privacy
in WirelessSensor Networks.
Synopsis:
Message authentication is one of the most effective ways to thwart unauthorized and
corrupted messages from being forwarded in wireless sensor networks (WSNs). For this
reason, many message authentication schemes have been developed, based on either
symmetric-key cryptosystems or public-key cryptosystems. Most of them, however, have
the limitations of high computational and communication overhead in addition to lack of
scalability and resilience to node compromise attacks. To address these issues, a
polynomial-based scheme was recently introduced. However, this scheme and its
extensions all have the weakness of a built-in threshold determined by the degree of the
polynomial: when the number of messages transmitted is larger than this threshold, the
adversary can fully recover the polynomial. In this paper, we propose a scalable
authentication scheme based on elliptic curve cryptography (ECC). While enabling
intermediate nodes authentication, our proposed scheme allows any node to transmit an
unlimited number of messages without suffering the threshold problem. In addition, our
scheme can also provide message source privacy. Both theoretical analysis and simulation
results demonstrate that our proposed scheme is more efficient than the polynomial-based
approach in terms of computational and communication overhead under comparable
security levels while providing message source privacy.
22. Nothing is for Free: Security in Searching Shared and
Encrypted Data.
Synopsis:
Most existing symmetric searchable encryption schemes aim at allowing a user to
outsource her encrypted data to a cloud server and delegate the latter to search on her

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behalf. These schemes do not qualify as a secure and scalable solution for the multiparty
setting, where users outsource their encrypted data to a cloud server and selectively
authorize each other to search. Due to the possibility that the cloud server may collude with
some malicious users, it is a challenge to have a secure and scalable multiparty searchable
encryption (MPSE) scheme. This is shown by our analysis on the Popa-Zeldovich scheme,
which says that an honest user may leak all her search patterns even if she shares only one
of her documents with another malicious user. Based on our analysis, we present a new
security model for MPSE by considering the worst case and average-case scenarios, which
capture different server-user collusion possibilities. We then propose a MPSE scheme by
employing the bilinear property of Type-3 pairings and prove its security based on the
bilinear Diffie-Hellman variant and symmetric external Diffie-Hellman assumptions in the
random oracle model.
23. On the Security of a Public Auditing Mechanism for
Shared Cloud Data Service.
Synopsis:
Recently, a public auditing protocol for shared data called Panda (IEEE Transactions on
Services Computing, doi: 10.1109/TSC.2013.2295611) was proposed to ensure the
correctness of the outsourced data. A distinctive feature of Panda is the support of data
sharing and user revocation. Unfortunately, in this letter, we show that Panda is insecure in
the sense that a cloud server can hide data loss without being detected. Specifically, we
show that even some stored file blocks have been lost, the server is able to generate a valid
proof by replacing a pair of lost data block and its signature with another block and
signature pair. We also provide a solution to the problem while preserving all the desirable
features of the original protocol.
24. On the Security of a Public Auditing Mechanism for
Shared Cloud Data Service.
Synopsis:
Recently, a public auditing protocol for shared data called Panda (IEEE Transactions on
Services Computing, doi: 10.1109/TSC.2013.2295611) was proposed to ensure the
correctness of the outsourced data. A distinctive feature of Panda is the support of data
sharing and user revocation. Unfortunately, in this letter, we show that Panda is insecure in
the sense that a cloud server can hide data loss without being detected. Specifically, we
show that even some stored file blocks have been lost, the server is able to generate a valid
proof by replacing a pair of lost data block and its signature with another block and

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signature pair. We also provide a solution to the problem while preserving all the desirable
features of the original protocol.
25.Label-Embedding for Attribute-Based Classification
Attributes are an intermediate representation, which enables parameter sharing between
classes, a must when training data is scarce. We propose to view attribute-based image
classification as a label-embedding problem: each class is embedded in the space of attribute
vectors. We introduce a function which measures the compatibility between an image and a
label embedding. The parameters of this function are learned on a training set of labeled
samples to ensure that, given an image, the correct classes rank higher than the incorrect ones.
Results on the Animals With Attributes and Caltech-UCSD-Birds datasets show that the
proposed framework outperforms the standard Direct Attribute Prediction baseline in a zero-shot
learning scenario. The label embedding framework offers other advantages such as the ability
to leverage alternative sources of information in addition to attributes (e.g. class hierarchies) or
to transition smoothly from zero-shot learning to learning with large quantities of data.
26. Load Rebalancing for Distributed File Systems in
Clouds.
Synopsis:
Distributed file systems are key building blocks for cloud computing applications based on
the MapReduce programming paradigm. In such file systems, nodes simultaneously serve
computing and storage functions; a file is partitioned into a number of chunks allocated in
distinct nodes so that MapReduce tasks can be performed in parallel over the nodes.
However, in a cloud computing environment, failure is the norm, and nodes may be
upgraded, replaced, and added in the system. Files can also be dynamically created,
deleted, and appended. This results in load imbalance in a distributed file system; that is,
the file chunks are not distributed as uniformly as possible among the nodes. Emerging
distributed file systems in production systems strongly depend on a central node for chunk
reallocation. This dependence is clearly inadequate in a large-scale, failure-prone
environment because the central load balancer is put under considerable workload that is
linearly scaled with the system size, and may thus become the performance bottleneck and
the single point of failure. In this paper, a fully distributed load rebalancing algorithm is
presented to cope with the load imbalance problem. Our algorithm is compared against a
centralized approach in a production system and a competing distributed solution presented
in the literature. The simulation results indicate that our proposal is comparable with the

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existing centralized approach and considerably outperforms the prior distributed algorithm in
terms of load imbalance factor, movement cost, and algorithmic overhead. The performance
of our proposal implemented in the Hadoop distributed file system is further investigated in
a cluster environment.
27. Message in a Sealed Bottle: Privacy Preserving Friending
in Social Networks.
Synopsis:
Many proximity-based mobile social networks are developed to facilitate connections
between any two people, or to help a user to find people with a matched profile within a
certain distance. A challenging task in these applications is to protect the privacy of the
participants' profiles and personal interests. In this paper, we design novel mechanisms,
when given a preference-profile submitted by a user, that search persons with matching-
profile in decentralized multi-hop mobile social networks. Our mechanisms also establish a
secure communication channel between the initiator and matching users at the time when
the matching user is found. Our rigorous analysis shows that our mechanism is privacy-
preserving (no participants' profile and the submitted preference-profile are exposed),
verifiable (both the initiator and the unmatched user cannot cheat each other to pretend to
be matched), and efficient in both communication and computation. Extensive evaluations
using real social network data, and actual system implementation on smart phones show
that our mechanisms are significantly more efficient than existing solutions.
28. Mona: Secure Multi-Owner Data Sharing for Dynamic
Groups in the Cloud
Synopsis:
With the character of low maintenance, cloud computing provides an economical and
efficient solution for sharing group resource among cloud users. Unfortunately, sharing data
in a multi-owner manner while preserving data and identity privacy from an untrusted cloud
is still a challenging issue, due to the frequent change of the membership. In this paper, we
propose a secure multi-owner data sharing scheme, named Mona, for dynamic groups in
the cloud. By leveraging group signature and dynamic broadcast encryption techniques, any
cloud user can anonymously share data with others. Meanwhile, the storage overhead and
encryption computation cost of our scheme are independent with the number of revoked
users. In addition, we analyze the security of our scheme with rigorous proofs, and
demonstrate the efficiency of our scheme in experiments.

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29. Securing Broker-Less Publish/Subscribe Systems Using
Identity-Based Encryption.
Synopsis:
The provisioning of basic security mechanisms such as authentication and confidentiality is
highly challenging in a content-based publish/subscribe system. Authentication of
publishers and subscribers is difficult to achieve due to the loose coupling of publishers and
subscribers. Likewise, confidentiality of events and subscriptions conflicts with content-
based routing. This paper presents a novel approach to provide confidentiality and
authentication in a broker-less content-based publish/subscribe system. The authentication
of publishers and subscribers as well as confidentiality of events is ensured, by adapting the
pairing-based cryptography mechanisms, to the needs of a publish/subscribe system.
Furthermore, an algorithm to cluster subscribers according to their subscriptions preserves
a weak notion of subscription confidentiality. In addition to our previous work , this paper
contributes 1) use of searchable encryption to enable efficient routing of encrypted events,
2) multicredential routing a new event dissemination strategy to strengthen the weak
subscription confidentiality, and 3) thorough analysis of different attacks on subscription
confidentiality. The overall approach provides fine-grained key management and the cost
for encryption, decryption, and routing is in the order of subscribed attributes. Moreover, the
evaluations show that providing security is affordable w.r.t. 1) throughput of the proposed
cryptographic primitives, and 2) delays incurred during the construction of the
publish/subscribe overlay and the event dissemination.
30. Shared Authority Based Privacy-Preserving
Authentication Protocol in Cloud Computing.
Synopsis:
Cloud computing is an emerging data interactive paradigm to realize users' data remotely
stored in an online cloud server. Cloud services provide great conveniences for the users to
enjoy the on-demand cloud applications without considering the local infrastructure
limitations. During the data accessing, different users may be in a collaborative relationship,
and thus data sharing becomes significant to achieve productive benefits. The existing
security solutions mainly focus on the authentication to realize that a user's privative data
cannot be illegally accessed, but neglect a subtle privacy issue during a user challenging
the cloud server to request other users for data sharing. The challenged access request
itself may reveal the user's privacy no matter whether or not it can obtain the data access
permissions. In this paper, we propose a shared authority based privacy-preserving
authentication protocol (SAPA) to address above privacy issue for cloud storage. In the
SAPA, 1) shared access authority is achieved by anonymous access request matching

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mechanism with security and privacy considerations (e.g., authentication, data anonymity,
user privacy, and forward security); 2) attribute based access control is adopted to realize
that the user can only access its own data fields; 3) proxy re-encryption is applied to provide
data sharing among the multiple users. Meanwhile, universal composability (UC) model is
established to prove that the SAPA theoretically has the design correctness. It indicates that
the proposed protocol is attractive for multi-user collaborative cloud applications.
31. SocialTube: P2P-assisted video sharing in online social
networks.
Synopsis:
Video sharing has been an increasingly popular application in online social networks
(OSNs). However, its sustainable development is severely hindered by the intrinsic limit of
the client/server architecture deployed in current OSN video systems, which is not only
costly in terms of server bandwidth and storage but also not scalable. The peer-assisted
Video-on-Demand (VOD) technique, in which participating peers assist the server in
delivering video content has been proposed recently. Unfortunately, videos can only be
disseminated through friends in OSNs. Therefore, current VOD works that explore
clustering nodes with similar interests or close location for high performance are suboptimal,
if not entirely inapplicable, in OSNs. Based on our long-term real-world measurement of
over 1,000,000 users and 2,500 videos in Facebook, we propose SocialTube, a novel peer-
assisted video sharing system that explores social relationship, interest similarity, and
physical location between peers in OSNs. Specifically, SocialTube incorporates three
algorithms: a social network (SN)-based P2P overlay construction algorithm, a SN-based
chunk prefetch algorithm, and a buffer management algorithm. The trace driven based
simulation results show that SocialTube can improve the quality of user experience and
system scalability over current P2P VOD techniques.
32. Optimal Multiserver Configuration for Profit
Maximization in Cloud Computing.
Synopsis:
As cloud computing becomes more and more popular, understanding the economics of
cloud computing becomes critically important. To maximize the profit, a service provider
should understand both service charges and business costs, and how they are determined
by the characteristics of the applications and the configuration of a multiserver system. The
problem of optimal multiserver configuration for profit maximization in a cloud computing
environment is studied. Our pricing model takes such factors into considerations as the
amount of a service, the workload of an application environment, the configuration of a

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multiserver system, the service-level agreement, the satisfaction of a consumer, the quality
of a service, the penalty of a low-quality service, the cost of renting, the cost of energy
consumption, and a service provider's margin and profit. Our approach is to treat a
multiserver system as an M/M/m queuing model, such that our optimization problem can be
formulated and solved analytically. Two server speed and power consumption models are
considered, namely, the idle-speed model and the constant-speed model. The probability
density function of the waiting time of a newly arrived service request is derived. The
expected service charge to a service request is calculated. The expected net business gain
in one unit of time is obtained. Numerical calculations of the optimal server size and the
optimal server speed are demonstrated.
33. Priority-Based Consolidation of Parallel Workloads in
the Cloud.
Synopsis:
The cloud computing paradigm is attracting an increased number of complex
applications to run in remote data centers. Many complex applications require parallel
processing capabilities. Parallel applications of certain nature often show a decreasing
utilization of CPU resources as parallelism grows, mainly because of the communication
and synchronization among parallel processes. It is challenging but important for a data
center to achieve a certain level of utilization of its nodes while maintaining the level of
responsiveness of parallel jobs. Existing parallel scheduling mechanisms normally take
responsiveness as the top priority and need nontrivial effort to make them work for data
centers in the cloud era. In this paper, we propose a priority-based method to consolidate
parallel workloads in the cloud. We leverage virtualization technologies to partition the
computing capacity of each node into two tiers, the foreground virtual machine (VM) tier
(with high CPU priority) and the background VM tier (with low CPU priority). We provide
scheduling algorithms for parallel jobs to make efficient use of the two tier VMs to improve
the responsiveness of these jobs. Our extensive experiments show that our parallel
scheduling algorithm significantly outperforms commonly used algorithms such as
extensible argonne scheduling system in a data center setting. The method is practical and
effective for consolidating parallel workload in data centers.
34. QoS Ranking Prediction for Cloud Services.
Synopsis:
Cloud computing is becoming popular. Building high-quality cloud applications is a critical
research problem. QoS rankings provide valuable information for making optimal cloud
service selection from a set of functionally equivalent service candidates. To obtain QoS

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values, real-world invocations on the service candidates are usually required. To avoid the
time-consuming and expensive real-world service invocations, this paper proposes a QoS
ranking prediction framework for cloud services by taking advantage of the past service
usage experiences of other consumers. Our proposed framework requires no additional
invocations of cloud services when making QoS ranking prediction. Two personalized QoS
ranking prediction approaches are proposed to predict the QoS rankings directly.
Comprehensive experiments are conducted employing real-world QoS data, including 300
distributed users and 500 real-world web services all over the world. The experimental
results show that our approaches outperform other competing approaches.
35. An Ontology-Based Hybrid Approach to Activity
Modeling for Smart Homes.
Synopsis:
Activity models play a critical role for activity recognition and assistance in ambient assisted
living. Existing approaches to activity modeling suffer from a number of problems, e.g., cold-
start, model reusability, and incompleteness. In an effort to address these problems, we
introduce an ontology-based hybrid approach to activity modeling that combines domain
knowledge based model specification and data-driven model learning. Central to the
approach is an iterative process that begins with “seed” activity models created by
ontological engineering. The “seed” models are deployed, and subsequently evolved
through incremental activity discovery and model update. While our previous work has
detailed ontological activity modeling and activity recognition, this paper focuses on the
systematic hybrid approach and associated methods and inference rules for learning new
activities and user activity profiles. The approach has been implemented in a feature-rich
assistive living system. Analysis of the experiments conducted has been undertaken in an
effort to test and evaluate the activity learning algorithms and associated mechanisms.
36. A Highly Scalable Key Pre-Distribution Scheme for
Wireless Sensor Networks.
Synopsis:
Given the sensitivity of the potential WSN applications and because of resource limitations,
key management emerges as a challenging issue for WSNs. One of the main concerns
when designing a key management scheme is the network scalability. Indeed, the protocol
should support a large number of nodes to enable a large scale deployment of the network.
In this paper, we propose a new scalable key management scheme for WSNs which
provides a good secure connectivity coverage. For this purpose, we make use of the unital
design theory. We show that the basic mapping from unitals to key pre-distribution allows us

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to achieve high network scalability. Nonetheless, this naive mapping does not guarantee a
high key sharing probability. Therefore, we propose an enhanced unital-based key pre-
distribution scheme providing high network scalability and good key sharing probability
approximately lower bounded by 1-e-1 ≈ 0.632. We conduct approximate analysis and
simulations and compare our solution to those of existing methods for different criteria such
as storage overhead, network scalability, network connectivity, average secure path length
and network resiliency. Our results show that the proposed approach enhances the network
scalability while providing high secure connectivity coverage and overall improved
performance. Moreover, for an equal network size, our solution reduces significantly the
storage overhead compared to those of existing solutions.
37. A Novel Anti phishing framework based on visual
cryptography.
Synopsis:
With the advent of internet, various online attacks has been increased and among them the
most popular attack is phishing. Phishing is an attempt by an individual or a group to get
personal confidential information such as passwords, credit card information from
unsuspecting victims for identity theft, financial gain and other fraudulent activities. Fake
websites which appear very similar to the original ones are being hosted to achieve this. In
this paper we have proposed a new approach named as "A Novel Anti-phishing framework
based on visual cryptography "to solve the problem of phishing. Here an image based
authentication using Visual Cryptography is implemented. The use of visual cryptography is
explored to preserve the privacy of an image captcha by decomposing the original image
captcha into two shares (known as sheets) that are stored in separate database
servers(one with user and one with server) such that the original image captcha can be
revealed only when both are simultaneously available; the individual sheet images do not
reveal the identity of the original image captcha. Once the original image captcha is
revealed to the user it can be used as the password. Using this website cross verifies its
identity and proves that it is a genuine website before the end users.
38. Computing Localized Power-Efficient Data Aggregation
Trees for Sensor Networks.
Synopsis:
We propose localized, self organizing, robust, and energy-efficient data aggregation tree
approaches for sensor networks, which we call Localized Power-Efficient Data Aggregation
Protocols (L-PEDAPs). They are based on topologies, such as LMST and RNG, that can
approximate minimum spanning tree and can be efficiently computed using only position or

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distance information of one-hop neighbors. The actual routing tree is constructed over these
topologies. We also consider different parent selection strategies while constructing a
routing tree. We compare each topology and parent selection strategy and conclude that
the best among them is the shortest path strategy over LMST structure. Our solution also
involves route maintenance procedures that will be executed when a sensor node fails or a
new node is added to the network. The proposed solution is also adapted to consider the
remaining power levels of nodes in order to increase the network lifetime. Our simulation
results show that by using our power-aware localized approach, we can almost have the
same performance of a centralized solution in terms of network lifetime, and close to 90
percent of an upper bound derived here.
39. Behavior Rule Specification-Based Intrusion Detection
for Safety Critical Medical Cyber Physical Systems.
Synopsis:
We propose and analyze a behavior-rule specification-based technique for intrusion
detection of medical devices embedded in a medical cyber physical system (MCPS) in
which the patient's safety is of the utmost importance. We propose a methodology to
transform behavior rules to a state machine, so that a device that is being monitored for its
behavior can easily be checked against the transformed state machine for deviation from its
behavior specification. Using vital sign monitor medical devices as an example, we
demonstrate that our intrusion detection technique can effectively trade false positives off
for a high detection probability to cope with more sophisticated and hidden attackers to
support ultra safe and secure MCPS applications. Moreover, through a comparative
analysis, we demonstrate that our behavior-rule specification-based IDS technique
outperforms two existing anomaly-based techniques for detecting abnormal patient
behaviors in pervasive healthcare applications.
40. Mobi-Sync: Efficient Time Synchronization for Mobile
Underwater Sensor Networks.
Synopsis:
Time synchronization is an important requirement for many services provided by distributed
networks. A lot of time synchronization protocols have been proposed for terrestrial
Wireless Sensor Networks (WSNs). However, none of them can be directly applied to
Underwater Sensor Networks (UWSNs). A synchronization algorithm for UWSNs must
consider additional factors such as long propagation delays from the use of acoustic
communication and sensor node mobility. These unique challenges make the accuracy of
synchronization procedures for UWSNs even more critical. Time synchronization solutions

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specifically designed for UWSNs are needed to satisfy these new requirements. This paper
proposes Mobi-Sync, a novel time synchronization scheme for mobile underwater sensor
networks. Mobi-Sync distinguishes itself from previous approaches for terrestrial WSN by
considering spatial correlation among the mobility patterns of neighboring UWSNs nodes.
This enables Mobi-Sync to accurately estimate the long dynamic propagation delays.
Simulation results show that Mobi-Sync outperforms existing schemes in both accuracy and
energy efficiency.
41. SPOC: A Secure and Privacy-Preserving Opportunistic
Computing Framework for Mobile-Healthcare Emergency.
Synopsis:
With the pervasiveness of smart phones and the advance of wireless body sensor networks
(BSNs), mobile Healthcare (m-Healthcare), which extends the operation of Healthcare
provider into a pervasive environment for better health monitoring, has attracted
considerable interest recently. However, the flourish of m-Healthcare still faces many
challenges including information security and privacy preservation. In this paper, we
propose a secure and privacy-preserving opportunistic computing framework, called SPOC,
for m-Healthcare emergency. With SPOC, smart phone resources including computing
power and energy can be opportunistically gathered to process the computing-intensive
personal health information (PHI) during m-Healthcare emergency with minimal privacy
disclosure. In specific, to leverage the PHI privacy disclosure and the high reliability of PHI
process and transmission in m-Healthcare emergency, we introduce an efficient user-
centric privacy access control in SPOC framework, which is based on an attribute-based
access control and a new privacy-preserving scalar product computation (PPSPC)
technique, and allows a medical user to decide who can participate in the opportunistic
computing to assist in processing his overwhelming PHI data. Detailed security analysis
shows that the proposed SPOC framework can efficiently achieve user-centric privacy
access control in m-Healthcare emergency. In addition, performance evaluations via
extensive simulations demonstrate the SPOC's effectiveness in term of providing high-
reliable-PHI process and transmission while minimizing the privacy disclosure during m-
Healthcare emergency.
42. Enabling Public Auditability and Data Dynamics for
Storage Security in Cloud Computing.
Synopsis:
Cloud Computing has been envisioned as the next-generation architecture of IT Enterprise.
It moves the application software and databases to the centralized large data centers,

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where the management of the data and services may not be fully trustworthy. This unique
paradigm brings about many new security challenges, which have not been well
understood. This work studies the problem of ensuring the integrity of data storage in Cloud
Computing. In particular, we consider the task of allowing a third party auditor (TPA), on
behalf of the cloud client, to verify the integrity of the dynamic data stored in the cloud. The
introduction of TPA eliminates the involvement of the client through the auditing of whether
his data stored in the cloud are indeed intact, which can be important in achieving
economies of scale for Cloud Computing. The support for data dynamics via the most
general forms of data operation, such as block modification, insertion, and deletion, is also a
significant step toward practicality, since services in Cloud Computing are not limited to
archive or backup data only. While prior works on ensuring remote data integrity often lacks
the support of either public auditability or dynamic data operations, this paper achieves
both. We first identify the difficulties and potential security problems of direct extensions
with fully dynamic data updates from prior works and then show how to construct an elegant
verification scheme for the seamless integration of these two salient features in our protocol
design. In particular, to achieve efficient data dynamics, we improve the existing proof of
storage models by manipulating the classic Merkle Hash Tree construction for block tag
authentication. To support efficient handling of multiple auditing tasks, we further explore
the technique of bilinear aggregate signature to extend our main result into a multiuser
setting, where TPA can perform multiple auditing tasks simultaneously. Extensive security
and performance analysis show that the proposed schemes are highly efficient and
provably secure.
43.COMIC: Cost Optimization for Internet Content
Multihoming.
Synopsis:
Content service is a type of Internet cloud service that provides end-users plentiful contents. To
ensure high performance for content delivering, content service utilizes a technology known as
content multihoming: contents are generated from multiplegeographically distributed data
centers and delivered by multiple distributed content distribution networks (CDNs). The
electricity costs for data centers and the usage costs for CDNs are major contributors to the
contents service cost. As electricity prices vary across data centers and usage costs vary
across CDNs, scheduling data centers and CDNs has a tremendous consequence for
optimizing content service cost. In this paper, we propose a novel framework named Cost
Optimization for Internet Content Multihoming (COMIC). COMIC dynamically balances end-
users’ loads among data centers and CDNs so as to minimize the content service cost. Using
real-lifeelectricity prices and CDN traces, the experiments demonstrate that COMIC effectively
reduces the content service cost by more than 20 percent.

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44. Privacy Preserving Back-Propagation Neural Network
Learning Made Practical with Cloud Computing.
Synopsis:
To improve the accuracy of learning result, in practice multiple parties may collaborate
through conducting joint Back-Propagation neural network learning on the union of their
respective data sets. During this process no party wants to disclose her/his private data to
others. Existing schemes supporting this kind of collaborative learning are either limited in
the way of data partition or just consider two parties. There lacks a solution that allows two
or more parties, each with an arbitrarily partitioned data set, to collaboratively conduct the
learning. This paper solves this open problem by utilizing the power of cloud computing. In
our proposed scheme, each party encrypts his/her private data locally and uploads the
ciphertexts into the cloud. The cloud then executes most of the operations pertaining to the
learning algorithms over ciphertexts without knowing the original private data. By securely
offloading the expensive operations to the cloud, we keep the computation and
communication costs on each party minimal and independent to the number of participants.
To support flexible operations over ciphertexts, we adopt and tailor the BGN "doubly
homomorphic" encryption algorithm for the multiparty setting. Numerical analysis and
experiments on commodity cloud show that our scheme is secure, efficient, and accurate.
45. Balancing the Trade-Offs between Query Delay and Data
Availability in MANETs.
Synopsis:
In mobile ad hoc networks (MANETs), nodes move freely and link/node failures are
common, which leads to frequent network partitions. When a network partition occurs,
mobile nodes in one partition are not able to access data hosted by nodes in other
partitions, and hence significantly degrade the performance of data access. To deal with
this problem, we apply data replication techniques. Existing data replication solutions in
both wired or wireless networks aim at either reducing the query delay or improving the data
availability, but not both. As both metrics are important for mobile nodes, we propose
schemes to balance the trade-offs between data availability and query delay under different
system settings and requirements. Extensive simulation results show that the proposed
schemes can achieve a balance between these two metrics and provide satisfying system
performance.
46. Exploiting Dynamic Resource Allocation for Efficient
Parallel Data Processing in the Cloud.

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Synopsis:
In recent years ad hoc parallel data processing has emerged to be one of the killer
applications for Infrastructure-as-a-Service (IaaS) clouds. Major Cloud computing
companies have started to integrate frameworks for parallel data processing in their product
portfolio, making it easy for customers to access these services and to deploy their
programs. However, the processing frameworks which are currently used have been
designed for static, homogeneous cluster setups and disregard the particular nature of a
cloud. Consequently, the allocated compute resources may be inadequate for big parts of
the submitted job and unnecessarily increase processing time and cost. In this paper, we
discuss the opportunities and challenges for efficient parallel data processing in clouds and
present our research project Nephele. Nephele is the first data processing framework to
explicitly exploit the dynamic resource allocation offered by today's IaaS clouds for both,
task scheduling and execution. Particular tasks of a processing job can be assigned to
different types of virtual machines which are automatically instantiated and terminated
during the job execution. Based on this new framework, we perform extended evaluations of
MapReduce-inspired processing jobs on an IaaS cloud system and compare the results to
the popular data processing framework Hadoop.
47. Exploiting Rateless Codes in Cloud Storage Systems.
Synopsis:
Block-level cloud storage (BLCS) offers to users and applications the access to persistent
block storage devices (virtual disks) that can be directly accessed and used as if they were
raw physical disks. In this paper we devise ENIGMA, an architecture for the back-end of
BLCS systems able to provide adequate levels of access and transfer performance,
availability, integrity, and confidentiality, for the data it stores. ENIGMA exploits LT rateless
codes to store fragments of sectors on storage nodes organized in clusters. We
quantitatively evaluate how the various ENIGMA system parameters affect the
performance, availability, integrity, and confidentiality of virtual disks. These evaluations are
carried out by using both analytical modeling (for availability, integrity, and confidentiality)
and discrete event simulation (for performance), and by considering a set of realistic
operational scenarios. Our results indicate that it is possible to simultaneously achieve all
the objectives set forth for BLCS systems by using ENIGMA, and that a careful choice of
the various system parameters is crucial to achieve a good compromise among them.
Moreover, they also show that LT coding-based BLCS systems outperform traditional BLCS
systems in all the aspects mentioned before.
48. Secure and Efficient Data Transmission for Cluster-
Based Wireless Sensor Networks.

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Synopsis:
Secure data transmission is a critical issue for wireless sensor networks (WSNs).
Clustering is an effective and practical way to enhance the system performance of WSNs.
In this paper, we study a secure data transmission for cluster-based WSNs (CWSNs),
where the clusters are formed dynamically and periodically. We propose two secure and
efficient data transmission (SET) protocols for CWSNs, called SET-IBS and SET-IBOOS, by
using the identity-based digital signature (IBS) scheme and the identity-based online/offline
digital signature (IBOOS) scheme, respectively. In SET-IBS, security relies on the hardness
of the Diffie-Hellman problem in the pairing domain. SET-IBOOS further reduces the
computational overhead for protocol security, which is crucial for WSNs, while its security
relies on the hardness of the discrete logarithm problem. We show the feasibility of the
SET-IBS and SET-IBOOS protocols with respect to the security requirements and security
analysis against various attacks. The calculations and simulations are provided to illustrate
the efficiency of the proposed protocols. The results show that the proposed protocols have
better performance than the existing secure protocols for CWSNs, in terms of security
overhead and energy consumption.
49. BECAN: A Bandwidth-Efficient Cooperative
Authentication Scheme for Filtering Injected False Data in
Synopsis:
Injecting false data attack is a well known serious threat to wireless sensor network, for which
an adversary reports bogus information to sink causing error decision at upper level and energy
waste in en-route nodes. In this paper, we propose a novel bandwidth-efficient cooperative
authentication (BECAN) scheme for filtering injected false data. Based on the random graph
characteristics of sensor node deployment and the cooperative bit-compressed authentication
technique, the proposed BECAN scheme can save energy by early detecting and filtering the
majority of injected false data with minor extra overheads at the en-route nodes. In addition,
only a very small fraction of injected false data needs to be checked by the sink, which thus
largely reduces the burden of the sink. Both theoretical and simulation results are given to
demonstrate the effectiveness of the proposed scheme in terms of high filtering probability and
energy saving.
50. Rumor Riding: Anonymizing Unstructured Peer-to-Peer
Systems.
Synopsis:

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Although anonymizing Peer-to-Peer (P2P) systems often incurs extra costs in terms of
transfer efficiency, many systems try to mask the identities of their users for privacy
considerations. Existing anonymity approaches are mainly path-based: peers have to pre-
construct an anonymous path before transmission. The overhead of maintaining and
updating such paths is significantly high. In this paper, we propose Rumor Riding (RR), a
lightweight mutual anonymity protocol for decentralized P2P systems. RR employs a
random walk scheme which frees initiating peers from the heavy load of path construction.
Compared with previous RSA-based anonymity approaches, RR also takes advantage of
lower cryptographic overhead by mainly utilizing a symmetric cryptographic algorithm to
achieve anonymity. We demonstrate the effectiveness of this design through trace-driven
simulations. The analytical and experimental results show that RR is more efficient than
existing protocols. We also discuss our early implementation experiences with the RR
prototype.
51. Exploiting Service Similarity for Privacy in Location-
Based Search Queries.
Synopsis:
Location-based applications utilize the positioning capabilities of a mobile device to
determine the current location of a user, and customize query results to include neighboring
points of interests. However, location knowledge is often perceived as personal information.
One of the immediate issues hindering the wide acceptance of location-based applications
is the lack of appropriate methodologies that offer fine grain privacy controls to a user
without vastly affecting the usability of the service. While a number of privacy-preserving
models and algorithms have taken shape in the past few years, there is an almost universal
need to specify one's privacy requirement without understanding its implications on the
service quality. In this paper, we propose a user-centric location-based service architecture
where a user can observe the impact of location inaccuracy on the service accuracy before
deciding the geo-coordinates to use in a query. We construct a local search application
based on this architecture and demonstrate how meaningful information can be exchanged
between the user and the service provider to allow the inference of contours depicting the
change in query results across a geographic area. Results indicate the possibility of large
default privacy regions (areas of no change in result set) in such applications.
Synopsis:

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53. BloomCast: Efficient and Effective Full-Text Retrieval in
Unstructured P2P Networks.
Synopsis:
Efficient and effective full-text retrieval in unstructured peer-to-peer networks remains a
challenge in the research community. First, it is difficult, if not impossible, for unstructured P2P
systems to effectively locate items with guaranteed recall. Second, existing schemes to improve
search success rate often rely on replicating a large number of item replicas across the wide
area network, incurring a large amount of communication and storage costs. In this paper, we
propose BloomCast, an efficient and effective full-text retrieval scheme, in unstructured P2P
networks. By leveraging a hybrid P2P protocol, BloomCast replicates the items uniformly at
random across the P2P networks, achieving a guaranteed recall at a communication cost of
O(√N), where N is the size of the network. Furthermore, by casting Bloom Filters instead of the
raw documents across the network, BloomCast significantly reduces the communication and
storage costs for replication. We demonstrate the power of BloomCast design through both
mathematical proof and comprehensive simulations based on the query logs from a major
commercial search engine and NIST TREC WT10G data collection. Results show that
BloomCast achieves an average query recall of 91 percent, which outperforms the existing WP
algorithm by 18 percent, while BloomCast greatly reduces the search latency for query
processing by 57 percent.

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54. Throughput and Delay Analysis for Convergecast with
MIMO in Wireless Networks.
Synopsis:
This paper investigates throughput and delay based on a traffic pattern, called convergecast,
where each of the n nodes in the network acts as a destination with k randomly chosen sources
corresponding to it. Adopting Multiple-Input-Multiple-Output (MIMO) technology, we devise two
many-to-one cooperative schemes under convergecast for both static and mobile ad hoc
networks (MANETs), respectively. We call them Convergimo Schemes. In static networks, our
Convergimo scheme highly utilizes hierarchical cooperation MIMO transmission. This feature
overcomes the bottleneck which hinders convergecast traffic from yielding ideal performance in
traditional ad hoc network, by turning the originally interfering signals into interference-resistant
ones. It helps to achieve an aggregate throughput up to Ω(n1-ϵ) for any ϵ >;0. In the mobile ad
hoc case, our Convergimo scheme characterizes on joint transmission from multiple nodes to
multiple receivers. With optimal network division where the number of nodes per cell is
constantly bounded, the achievable per-node throughput can reach Θ(1) with the corresponding
delay reduced to Θ(k). The gain comes from the strong and intelligent cooperation between
nodes in our scheme, along with the maximum number of concurrent active cells and the
shortest waiting time before transmission for each node within a cell. This increases the
chances for each destination to receive the data it needs with minimum overhead on extra
transmission. Moreover, our converge-based analysis well unifies and generalizes previous
work since the results derived from convergecast in our schemes can also cover other traffic
patterns. Last but not the least, our schemes are of interest not only from a theoretical
perspective but also provide useful theoretical guidelines to future design of MIMO schemes in
wireless networks.
55. Link Quality Aware Code Dissemination in Wireless
Sensor Networks.
Synopsis:
Wireless reprogramming is a crucial technique for software deployment in wireless sensor
networks (WSNs). Code dissemination is a basic building block to enable wireless
reprogramming. We present ECD, an Efficient Code Dissemination protocol leveraging 1-
hop link quality information based on the TinyOS platform. Compared to prior works, ECD
has three salient features. First, it supports dynamically configurable packet sizes. By
increasing the packet size for high PHY rate radios, it significantly improves the
transmission efficiency. Second, it employs an accurate sender selection algorithm to
mitigate transmission collisions and transmissions over poor links. Third, it employs a
simple impact-based backoff timer design to shorten the time spent in coordinating multiple

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eligible senders so that the largest impact sender is most likely to transmit. We implement
ECD based on TinyOS and evaluate its performance extensively via testbed experiments
and simulations. Results show that ECD outperforms state-of-the-art protocols, Deluge and
MNP, in terms of completion time and data traffic (e.g., about 20 percent less traffic and 20-
30 percent shorter completion time compared to Deluge).
56. Enabling Secure and Efficient Ranked Keyword Search
over Outsourced Cloud Data.
Synopsis:
Cloud computing economically enables the paradigm of data service outsourcing. However,
to protect data privacy, sensitive cloud data have to be encrypted before outsourced to the
commercial public cloud, which makes effective data utilization service a very challenging
task. Although traditional searchable encryption techniques allow users to securely search
over encrypted data through keywords, they support only Boolean search and are not yet
sufficient to meet the effective data utilization need that is inherently demanded by large
number of users and huge amount of data files in cloud. In this paper, we define and solve
the problem of secure ranked keyword search over encrypted cloud data. Ranked search
greatly enhances system usability by enabling search result relevance ranking instead of
sending undifferentiated results, and further ensures the file retrieval accuracy. Specifically,
we explore the statistical measure approach, i.e., relevance score, from information retrieval
to build a secure searchable index, and develop a one-to-many order-preserving mapping
technique to properly protect those sensitive score information. The resulting design is able
to facilitate efficient server-side ranking without losing keyword privacy. Thorough analysis
shows that our proposed solution enjoys “as-strong-as-possible” security guarantee
compared to previous searchable encryption schemes, while correctly realizing the goal of
ranked keyword search. Extensive experimental results demonstrate the efficiency of the
proposed solution.
57. In Cloud, Can Scientific Communities Benefit from the
Economies of Scale?.
Synopsis:
The basic idea behind cloud computing is that resource providers offer elastic resources to
end users. In this paper, we intend to answer one key question to the success of cloud
computing: in cloud, can small-to-medium scale scientific communities benefit from the
economies of scale? Our research contributions are threefold: first, we propose an
innovative public cloud usage model for small-to-medium scale scientific communities to
utilize elastic resources on a public cloud site while maintaining their flexible system

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controls, i.e., create, activate, suspend, resume, deactivate, and destroy their high-level
management entities-service management layers without knowing the details of
management. Second, we design and implement an innovative system-DawningCloud, at
the core of which are lightweight service management layers running on top of a common
management service framework. The common management service framework of
DawningCloud not only facilitates building lightweight service management layers for
heterogeneous workloads, but also makes their management tasks simple. Third, we
evaluate the systems comprehensively using both emulation and real experiments. We
found that for four traces of two typical scientific workloads: High-Throughput Computing
(HTC) and Many-Task Computing (MTC), DawningCloud saves the resource consumption
maximally by 59.5 and 72.6 percent for HTC and MTC service providers, respectively, and
saves the total resource consumption maximally by 54 percent for the resource provider
with respect to the previous two public cloud solutions. To this end, we conclude that small-
to-medium scale scientific communities indeed can benefit from the economies of scale of
public clouds with the support of the enabling system.
58. QoS Aware Geographic Opportunistic Routing in
Synopsis:
QoS routing is an important research issue in wireless sensor networks (WSNs), especially
for mission-critical monitoring and surveillance systems which requires timely and reliable
data delivery. Existing work exploits multipath routing to guarantee both reliability and delay
QoS constraints in WSNs. However, the multipath routing approach suffers from a
significant energy cost. In this work, we exploit the geographic opportunistic routing (GOR)
for QoS provisioning with both end-to-end reliability and delay constraints in WSNs. Existing
GOR protocols are not efficient for QoS provisioning in WSNs, in terms of the energy
efficiency and computation delay at each hop. To improve the efficiency of QoS routing in
WSNs, we define the problem of efficient GOR for multiconstrained QoS provisioning in
WSNs, which can be formulated as a multiobjective multiconstraint optimization problem.
Based on the analysis and observations of different routing metrics in GOR, we then
propose an Efficient QoS-aware GOR (EQGOR) protocol for QoS provisioning in WSNs.
EQGOR selects and prioritizes the forwarding candidate set in an efficient manner, which is
suitable for WSNs in respect of energy efficiency, latency, and time complexity. We
comprehensively evaluate EQGOR by comparing it with the multipath routing approach and
other baseline protocols through ns-2 simulation and evaluate its time complexity through
measurement on the MicaZ node. Evaluation results demonstrate the effectiveness of the
GOR approach for QoS provisioning in WSNs. EQGOR significantly improves both the end-
to-end energy efficiency and latency, and it is characterized by the low time complexity.

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59. Privacy-preserving multi-keyword ranked search over
encrypted cloud data.
Synopsis:
With the advent of cloud computing, data owners are motivated to outsource their complex
data management systems from local sites to the commercial public cloud for great
flexibility and economic savings. But for protecting data privacy, sensitive data has to be
encrypted before outsourcing, which obsoletes traditional data utilization based on plaintext
keyword search. Thus, enabling an encrypted cloud data search service is of paramount
importance. Considering the large number of data users and documents in the cloud, it is
necessary to allow multiple keywords in the search request and return documents in the
order of their relevance to these keywords. Related works on searchable encryption focus
on single keyword search or Boolean keyword search, and rarely sort the search results. In
this paper, for the first time, we define and solve the challenging problem of privacy-
preserving multi-keyword ranked search over encrypted cloud data (MRSE). We establish a
set of strict privacy requirements for such a secure cloud data utilization system. Among
various multi-keyword semantics, we choose the efficient similarity measure of “coordinate
matching”, i.e., as many matches as possible, to capture the relevance of data documents
to the search query. We further use “inner product similarity” to quantitatively evaluate such
similarity measure. We first propose a basic idea for the MRSE based on secure inner
product computation, and then give two significantly improved MRSE schemes to achieve
various stringent privacy requirements in two different threat models. Thorough analysis
investigating privacy and efficiency guarantees of proposed schemes is given. Experiments
on the real-world dataset further show proposed schemes indeed introduce low overhead
on computation and communication.
60. Expert Discovery and Interactions in Mixed Service-
Oriented Systems.
Synopsis:
Web-based collaborations and processes have become essential in today's business
environments. Such processes typically span interactions between people and services across
globally distributed companies. Web services and SOA are the defacto technology to implement
compositions of humans and services. The increasing complexity of compositions and the
distribution of people and services require adaptive and context-aware interaction models. To
support complex interaction scenarios, we introduce a mixed service-oriented system composed
of both human-provided and Software-Based Services (SBSs) interacting to perform joint
activities or to solve emerging problems. However, competencies of people evolve over time,
thereby requiring approaches for the automated management of actor skills, reputation, and

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trust. Discovering the right actor in mixed service-oriented systems is challenging due to scale
and temporary nature of collaborations. We present a novel approach addressing the need for
flexible involvement of experts and knowledge workers in distributed collaborations. We argue
that the automated inference of trust between members is a key factor for successful
collaborations. Instead of following a security perspective on trust, we focus on dynamic trust in
collaborative networks. We discuss Human-Provided Services (HPSs) and an approach for
managing user preferences and network structures. HPS allows experts to offer their skills and
capabilities as services that can be requested on demand. Our main contributions center around
a context-sensitive trust-based algorithm called ExpertHITS inspired by the concept of hubs and
authorities in web-based environments. ExpertHITS takes trust-relations and link properties in
social networks into account to estimate the reputation of users.
61. Dynamic Authentication for Cross-Realm SOA-Based
Business Processes.
Synopsis:
Modern distributed applications are embedding an increasing degree of dynamism, from
dynamic supply-chain management, enterprise federations, and virtual collaborations to
dynamic resource acquisitions and service interactions across organizations. Such
dynamism leads to new challenges in security and dependability. Collaborating services in a
system with a Service-Oriented Architecture (SOA) may belong to different security realms
but often need to be engaged dynamically at runtime. If their security realms do not have a
direct cross-realm authentication relationship, it is technically difficult to enable any secure
collaboration between the services. A potential solution to this would be to locate
intermediate realms at runtime, which serve as an authentication path between the two
separate realms. However, the process of generating an authentication path for two
distributed services can be highly complicated. It could involve a large number of extra
operations for credential conversion and require a long chain of invocations to intermediate
services. In this paper, we address this problem by designing and implementing a new
cross-realm authentication protocol for dynamic service interactions, based on the notion of
service-oriented multiparty business sessions. Our protocol requires neither credential
conversion nor establishment of any authentication path between the participating services
in a business session. The correctness of the protocol is formally analyzed and proven, and
an empirical study is performed using two production-quality Grid systems, Globus 4 and
CROWN. The experimental results indicate that the proposed protocol and its
implementation have a sound level of scalability and impose only a limited degree of
performance overhead, which is for example comparable with those security-related
overheads in Globus 4.

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62. Recursive Linear and Differential Cryptanalysis of
Ultralightweight Authentication Protocols.
Synopsis:
Privacy is faced with serious challenges in the ubiquitous computing world. In order to
handle this problem, some researchers in recent years have focused on design and
analysis of privacy-friendly ultralightweight authentication protocols. Although the majority of
these schemes have been broken to a greater or lesser extent, most of these attacks are
based on ad-hoc methods that are not extensible to a large class of ultralightweight
protocols. So this research area still suffers from the lack of structured cryptanalysis and
evaluation methods. In this paper, we introduce new frameworks for full disclosure attacks
on ultralightweight authentication protocols based on new concepts of recursive linear and
recursive differential cryptanalysis. The recursive linear attack is passive, deterministic, and
requires only a single authentication session, if it can be applied successfully. The recursive
differential attack is more powerful and can be applied to the protocols on which the linear
attack may not work. This attack is probabilistic, active in the sense that the attacker
suffices only to block some specific messages, and requires a few authentication sessions.
Having introduced these frameworks in a general view, we apply them on some well-known
ultralightweight protocols. The first attack can retrieve all the secret data of Yeh and SLMAP
authentication protocols and the second one can retrieve all the secret data of LMAP++,
SASI, and David-Prasad authentication protocols.
63. Secure Deduplication with Efficient and Reliable
Convergent Key Management.
Synopsis:
Data deduplication is a technique for eliminating duplicate copies of data, and has been
widely used in cloud storage to reduce storage space and upload bandwidth. Promising as
it is, an arising challenge is to perform secure deduplication in cloud storage. Although
convergent encryption has been extensively adopted for secure deduplication, a critical
issue of making convergent encryption practical is to efficiently and reliably manage a huge
number of convergent keys. This paper makes the first attempt to formally address the
problem of achieving efficient and reliable key management in secure deduplication. We
first introduce a baseline approach in which each user holds an independent master key for
encrypting the convergent keys and outsourcing them to the cloud. However, such a
baseline key management scheme generates an enormous number of keys with the
increasing number of users and requires users to dedicatedly protect the master keys. To
this end, we propose Dekey , a new construction in which users do not need to manage any
keys on their own but instead securely distribute the convergent key shares across multiple

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servers. Security analysis demonstrates that Dekey is secure in terms of the definitions
specified in the proposed security model. As a proof of concept, we implement Dekey using
the Ramp secret sharing scheme and demonstrate that Dekey incurs limited overhead in
realistic environments.
64. Query Access Assurance in Outsourced Databases.
Synopsis:
Query execution assurance is an important concept in defeating lazy servers in the
database as a service model. We show that extending query execution assurance to
outsourced databases with multiple data owners is highly inefficient. To cope with lazy
servers in the distributed setting, we propose query access assurance (Qaa) that focuses
on IO-bound queries. The goal in Qaa is to enable clients to verify that the server has
honestly accessed all records that are necessary to compute the correct query answer, thus
eliminating the incentives for the server to be lazy if the query cost is dominated by the IO
cost in accessing these records. We formalize this concept for distributed databases, and
present two efficient schemes that achieve Qaa with high success probabilities. The first
scheme is simple to implement and deploy, but may incur excessive server to client
communication cost and verification cost at the client side, when the query selectivity or the
database size increases. The second scheme is more involved, but successfully addresses
the limitation of the first scheme. Our design employs a few number theory techniques.
Extensive experiments demonstrate the efficiency, effectiveness, and usefulness of our
schemes.
65. Random4: An Application Specific Randomized
Encryption Algorithm to Prevent SQL Injection.
Synopsis:
Web Applications form an integral part of our day to day life. The number of attacks on
websites and the compromise of many individuals' secure data are increasing at an
alarming rate. With the advent of social networking and e-commerce, Web security attacks
such as phishing and spamming have become quite common. The consequences of these
attacks are ruthless. Hence, providing increased amount of security for the users and their
data becomes essential. Most important vulnerability as described in top 10 web security
issues by Open Web Application Security Project is SQL Injection Attack (SQLIA) [3]. This
paper focuses on how the advantages of randomization can be employed to prevent SQL
injection attacks in web based applications. SQL injection can be used for unauthorized
access to a database to penetrate the application illegally, modify the database or even
remove it. For a hacker to modify a database, details such as field and table names are

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required. So we try to propose a solution to the above problem by preventing it using an
encryption algorithm based on randomization. It has better performance and provides
increased security in comparison to the existing solutions. Also the time to crack the
database takes more time when techniques such as dictionary and brute force attack are
deployed. Our main aim is to provide increased security by developing a tool which
prevents illegal access to the database.
66. REMO: Resource-Aware Application State Monitoring
for Large-Scale Distributed Systems.
Synopsis:
To observe, analyze and control large scale distributed systems and the applications hosted
on them, there is an increasing need to continuously monitor performance attributes of
distributed system and application states. This results in application state monitoring tasks
that require fine-grained attribute information to be collected from relevant nodes efficiently.
Existing approaches either treat multiple application state monitoring tasks independently
and build ad-hoc monitoring trees for each task, or construct a single static monitoring tree
for multiple tasks. We argue that a careful planning of multiple application state monitoring
tasks by jointly considering multi-task optimization and node level resource constraints can
provide significant gains in performance and scalability. In this paper, we present REMO, a
REsource-aware application state MOnitoring system. REMO produces a forest of
optimized monitoring trees through iterations of two phases, one phase exploring cost
sharing opportunities via estimation and the other refining the monitoring plan through
resource-sensitive tree construction. Our experimental results include those gathered by
deploying REMO on a BlueGene/P rack running IBM's large-scale distributed streaming
system - System S. Using REMO running over 200 monitoring tasks for an application
deployed across 200 nodes results in a 35%-45% decrease in the percentage error of
collected attributes compared to existing schemes.
67. Uniform Embedding for Efficient JPEG Steganography.
Synopsis:
Steganography is the science and art of covert communication, which aims to hide the
secret messages into a cover medium while achieving the least possible statistical
detectability. To this end, the framework of minimal distortion embedding is widely adopted
in the development of the steganographic system, in which a well designed distortion
function is of vital importance. In this paper, a class of new distortion functions known as

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uniform embedding distortion function (UED) is presented for both side-informed and non
side-informed secure JPEG steganography. By incorporating the syndrome trellis coding,
the best codeword with minimal distortion for a given message is determined with UED,
which, instead of random modification, tries to spread the embedding modification uniformly
to quantized discrete cosine transform (DCT) coefficients of all possible magnitudes. In this
way, less statistical detectability is achieved, owing to the reduction of the average changes
of the first- and second-order statistics for DCT coefficients as a whole. The effectiveness of
the proposed scheme is verified with evidence obtained from exhaustive experiments using
popular steganalyzers with various feature sets on the BOSSbase database. Compared
with prior arts, the proposed scheme gains favorable performance in terms of secure
embedding capacity against steganalysis.
Synopsis:
69. Web Service Recommendation via Exploiting Location
and QoS Information.

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Synopsis:
Web services are integrated software components for the support of interoperable machine-
to-machine interaction over a network. Web services have been widely employed for
building service-oriented applications in both industry and academia in recent years. The
number of publicly available Web services is steadily increasing on the Internet. However,
this proliferation makes it hard for a user to select a proper Web service among a large
amount of service candidates. An inappropriate service selection may cause many
problems (e.g., ill-suited performance) to the resulting applications. In this paper, we
propose a novel collaborative filtering-based Web service recommender system to help
users select services with optimal Quality-of-Service (QoS) performance. Our recommender
system employs the location information and QoS values to cluster users and services, and
makes personalized service recommendation for users based on the clustering results.
Compared with existing service recommendation methods, our approach achieves
considerable improvement on the recommendation accuracy. Comprehensive experiments
are conducted involving more than 1.5 million QoS records of real-world Web services to
demonstrate the effectiveness of our approach.

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