Secure distributed deduplication systems with improved reliability

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SECURE DISTRIBUTED DEDUPLICATION SYSTEMS WITH IMPROVED RELIABILITY
ABSTRACT
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. However, there is only
one copy for each file stored in cloud even if such a file is owned by a huge number of users. As
a result, deduplication system improves storage utilization while reducing reliability.
Furthermore, the challenge of privacy for sensitive data also arises when they are outsourced
by users to cloud. Aiming to address the above security challenges, this paper makes the first
attempt to formalize the notion of distributed reliable deduplication system. We propose new
distributed deduplication systems with higher reliability in which the data chunks are
distributed across multiple cloud servers. The security requirements of data confidentiality and
tag consistency are also achieved by introducing a deterministic secret sharing scheme in
distributed storage systems, instead of using convergent encryption as in previous
deduplication systems. Security analysis demonstrates that our deduplication systems are
secure in terms of the definitions specified in the proposed security model. As a proof of
concept, we implement the proposed systems and demonstrate that the incurred overhead is
very limited in realistic environments.
MODULEDESCRIPTION:
NUMBER OF MODULES:
After careful analysis the system has been identified to have the following
modules:

1. Secure Deduplication
2. User Behavior Profiling:
3.Decoy documents.
1. Secure Deduplication:
Data deduplication is a specialized data compression technique for
eliminating duplicate copies of repeating data. Related and somewhat
synonymous terms are intelligent (data) compression and single-instance
(data) storage. This technique is used to improve storage utilization and
can also be applied to network data transfers to reduce the number of
bytes that must be sent. In the deduplication process, unique chunks of
data, or byte patterns, are identified and stored during a process of
analysis. As the analysis continues, other chunks are compared to the
stored copy and whenever a match occurs, the redundant chunk is
replaced with a small reference that points to the stored chunk. Given
that the same byte pattern may occur dozens, hundreds, or even
thousands of times (the match frequency is dependent on the chunk

size), the amount of data that must be stored or transferred can be greatly
reduced.
This type of deduplication is different from that performed by standard
file-compression tools, such as LZ77 and LZ78. Whereas these tools
identify short repeated substrings inside individual files, the intent of
storage-based data deduplication is to inspect large volumes of data and
identify large sections – such as entire files or large sections of files –
that are identical, in order to store only one copy of it. This copy may be
additionally compressed by single-file compression techniques. For
example a typical email system might contain 100 instances of the same
1 MB (megabyte) file attachment. Each time the email platform is
backed up, all 100 instances of the attachment are saved, requiring 100
MB storage space.
2. User Behavior Profiling:

We monitor data access in the cloud and detect abnormal data access patterns User profiling
is a well known Technique that can be applied here to model how, when, and how much a user accesses
their information in the Cloud. Such ‘normal user’ behavior can be continuously checked to determine
whether abnormal access to a user’s information is occurring. This method of behavior-based security is
commonly used in fraud detection applications. Such profiles would naturally include volumetric
information, how many documents are typically read and how often. We monitor for abnormal search
behaviors that exhibit deviations from the user baseline the correlation of search behavior anomaly
detection with trap-based decoy files should provide stronger evidence of malfeasance, and therefore
improve adetector’saccuracy.
3.Decoy documents.
We propose a different approach for securing data in the cloud using offensive decoy
technology. We monitor data access in the cloud and detect abnormal data access patterns. We launch
a disinformation attack by returning large amounts of decoy information to the attacker. This protects
against the misuse of the user’s real data. We use this technology to launch disinformation attacks
against malicious insiders, preventing them from distinguishing the real sensitive customer data from
fake worthlessdata the decoys,then,serve twopurposes:
(1) Validatingwhetherdataaccessis authorizedwhenabnormalinformationaccessisdetected,and
(2) Confusingthe attackerwithbogusinformation.

EXISTING SYSTEM
The various kinds of data for each user stored in the cloud and the demand of
long term continuous assurance of their data safety, the problem of verifying
correctness of data storage in the cloud becomes even more challenging. Cloud
Computing is not just a third party data warehouse. The data stored in the cloud
may be frequently updated by the users, including insertion, deletion,
modification, appending, reordering, etc. One critical challenge of today’s cloud
storage services is the management of the ever-increasing volume of data.
According to the analysis report of IDC, the volume of data in the wild is expected
to reach 40 trillion gigabytes in 2020. The baseline approach suffers two critical
deployment issues. First, it is inefficient, as it will generate an enormous number
of keys with the increasing number of users. Specifically, each user must associate
an encrypted convergent key with each block of its outsourced encrypted data
copies, so as to later restore the data copies. Although different users may share
the same data copies, they must have their own set of convergent keys so that no
other users can access their files. Second, the baseline approach is unreliable, as it
requires each user to dedicatedly protect his own master key. If the master key is
accidentally lost, then the user data cannot be recovered; if it is compromised by
attackers, then the user data will be leaked.
.
PROPOSED SYSTEM:

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 servers. Dekey using the Ramp
secret sharing scheme and demonstrate that Dekey incurs limited
overhead in realistic environments we propose a new construction called
Dekey, which provides efficiency and reliability guarantees for
convergent key management on both user and cloud storage sides. A
new construction Dekey is proposed to provide efficient and reliable
convergent key management through convergent key Deduplication and
secret sharing. Dekey supports both file-level Deduplication. Security
analysis demonstrates that Dekey is secure in terms of the definitions
specified in the proposed security model. In particular, Dekey remains
secure even the adversary controls a limited number of key servers. We
implement Dekey using the secret sharing scheme that enables the key
management to adapt to different reliability and confidentiality levels.

Our evaluation demonstrates that Dekey incurs limited overhead in
normal upload/download operations in realistic cloud environments.
The advantages of placing decoys in a file system are threefold:
(1) The detection of masquerade activity.
(2) The confusion of the attacker and the additional costs incurred to
distinguish real from bogus information, and
(3) The deterrence effect which, although hard to measure, plays a
significant role in preventing masquerade activity by risk-averse
attackers.
System Configuration:
HARDWARE REQUIREMENTS:
Hardware - Pentium
Speed - 1.1 GHz

RAM - 1GB
Hard Disk - 20 GB
KeyBoard - Standard WindowsKeyboard
Mouse - Two or Three ButtonMouse
Monitor - SVGA
SOFTWARE REQUIREMENTS:
OperatingSystem : Windows
Technology : Java and J2EE
Web Technologies : Html,JavaScript,CSS
IDE : My Eclipse
Web Server : Tomcat
Tool kit : AndroidPhone
Database : My SQL
Java Version : J2SDK1.5

Secure distributed deduplication systems with improved reliability

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Secure distributed deduplication systems with improved reliability