Homomorphic Encryption: Unlocking Privacy-Preserving Computation

In today’s data-driven world, balancing data privacy with the need to extract value from sensitive information is a common challenge across industries. Privacy-Enhancing Technologies (PETs) offer innovative solutions that help organisations gain insights from their data while preserving individual privacy.

One notable example of these technologies is Homomorphic Encryption (HE), which allows data to remain encrypted even as it’s analysed, enabling meaningful computation without exposing the original, sensitive information. PETs like HE are transforming how we can securely work with data in fields like healthcare, finance, and beyond.

We will explore what homomorphic encryption is, how it works, and how it could be applied. We’ll also touch on real-world applications, libraries to make implementation easier, and the potential challenges associated with using it.

What is Homomorphic Encryption, Non-Technically Speaking?

In simple terms, homomorphic encryption allows you to keep your data hidden while still letting someone perform calculations on it. The data remains fully encrypted, so no one can see it, but you can still get useful results from the computations. Once the calculations are done, only someone with the correct key can unlock the hidden and reveal the final result.

For example, imagine a company wants to analyse encrypted customer data to generate marketing insights. Homomorphic encryption allows this analysis to happen while the customer data stays encrypted the entire time. The company can still get the insights they need, but sensitive personal information remains protected.

What is Homomorphic Encryption, Technically Speaking?

At its core, homomorphic encryption allows computations to be performed on encrypted data without needing to decrypt it first. The data remains in its encrypted form throughout the entire process, and the computations themselves are also carried out on this encrypted data. When the encrypted result is decrypted, it will match what would have been produced if the operations had been performed on the unencrypted data.

This technique ensures that sensitive information stays protected throughout the computation process, making it highly valuable for privacy-sensitive applications, such as processing personal data in healthcare or financial services.

There are three types of homomorphic encryption, each suited to different use cases:

• Fully Homomorphic Encryption (FHE): Supports unlimited mathematical operations (addition, multiplication) on encrypted data. It’s the most powerful but computationally expensive.
• Somewhat Homomorphic Encryption (SHE): Supports a limited number of operations before the ciphertext becomes too noisy (i.e., inaccurate due to added cryptographic complexity). It offers a balance between security and performance.
• Partial Homomorphic Encryption (PHE): Allows only a single type of operation (either addition or multiplication) but not both. It’s simpler and faster than FHE but more limited in application.

Some homomorphic encryption schemes, such as CKKS (Cheon-Kim-Kim-Song scheme), trade off a small degree of precision for increased computational efficiency. CKKS allows computations on encrypted real and complex numbers, making it well-suited for practical applications like AI and machine learning. It introduces controlled noise into the results to enable faster processing, especially for operations requiring approximate arithmetic, such as in AI tasks. This trade-off between precision and speed makes CKKS more efficient for large-scale encrypted computations

What is Homomorphic Encryption, Explain It To Me Like I’m 5?

Homomorphic encryption allows someone to work with data that looks like nonsense without ever seeing or accessing the real data. The original data stays safe and hidden, but you can still do useful work on the nonsense version and get meaningful results.

When Would You Use It?

Homomorphic encryption is particularly valuable in situations where data secrecy is essential, but there’s still a need to perform complex calculations or analysis on that data. The key advantage of this encryption method is that it allows sensitive information to remain encrypted and hidden throughout the entire computation process, ensuring privacy at every stage.

Whether dealing with encrypted data for machine learning models, large-scale analytics, or collaborative research, homomorphic encryption enables meaningful insights without exposing the underlying raw data.

Use Cases

Healthcare Research: When sensitive data, such as patient medical records or genomic data, needs to be analysed. Homomorphic encryption allows researchers to perform computations on this encrypted data, enabling collaborative studies without compromising patient privacy.

Cloud Computing: If sensitive data is being outsourced to a third-party cloud provider for processing (such as AI training or data analytics), homomorphic encryption ensures the data remains protected during computation, even when stored or processed by external providers.

Financial Services: For secure operations like credit scoring or fraud detection, encrypted financial data can be processed without exposing sensitive customer information. Homomorphic encryption helps financial institutions analyse encrypted transaction data securely.

Research Collaboration: In cases where multiple organisations want to collaborate on shared data (e.g., a university and a pharmaceutical company), homomorphic encryption enables computation on encrypted datasets from different parties without exposing raw data.

Who Implements It?

While homomorphic encryption remains a technically advanced area, using libraries like Zama, Microsoft SEAL, or HElib significantly simplifies the process. However, expertise from various technical roles is still important for real-world applications:

Data Scientists are typically involved in understanding and applying the appropriate encryption schemes based on the use case. With the above libraries, the need for deep cryptographic design is reduced, but data scientists will still need to configure and use these libraries effectively for specific applications.

Software Engineers and Cloud Architects are essential for integrating and scaling homomorphic encryption within existing infrastructures, such as cloud platforms or data pipelines. Even with pre-built libraries, engineers need to ensure performance optimisation and compatibility with other systems.

Available Libraries and Tools

Several open-source libraries and tools make implementing homomorphic encryption easier:

Zama: Offers an open-source solution tailored for privacy-preserving machine learning and AI applications.

Microsoft SEAL: A widely-used library for building homomorphic encryption solutions, well-suited for research and real-world applications.

HElib: An IBM-developed library supporting fully homomorphic encryption, widely used in academic and research contexts.

PALISADE: Another open-source library offering a range of encryption solutions, including homomorphic encryption.

These libraries provide building blocks for implementing homomorphic encryption in practical scenarios, saving teams from building from scratch.

What Does This Unlock?

By enabling computations on encrypted data, homomorphic encryption unlocks:

• Secure collaboration across organisations: Multiple parties can work together on shared datasets (e.g., joint medical research) without ever exposing the raw data.
• Safe cloud outsourcing: Homomorphic encryption makes it possible to utilise cloud services for sensitive data processing without risking data exposure.

What Are the Downsides?

Despite its potential, homomorphic encryption comes with several downsides:

• Performance Overhead: It is computationally demanding and significantly slower than traditional encryption methods. This makes it less practical for real-time applications or scenarios that require high speed data processing.
• Energy Consumption: The heavy computational load also translates to increased energy consumption.
• Latency Issues: In real-time applications the latency introduced by these encryption methods can make them impractical.

How Difficult is it to Implement?

Implementing homomorphic encryption is somewhat technically challenging. Fully homomorphic encryption (FHE), in particular, is computationally intensive and requires significant infrastructure and expertise. However, the availability of well-documented libraries like SEAL, HElib, and PALISADE as well as Zama has made it easier to experiment with and adopt this technology, especially in research and academic settings.

TL;DR

Homomorphic encryption is a powerful PET that allows organisations to perform computations on encrypted data, preserving privacy without sacrificing utility. Although there are challenges, such as performance trade-offs and implementation complexity, its potential for privacy preserving data analysis is immense, especially in sectors like healthcare ,research, and finance.