Homomorphic Encryption: Crumpled Potential or Soaring Innovation?

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Content

• What is Homomorphic encryption?
• Different types of Homomorphic Encryption
• Why is Homomorphic Encryption Important to Everyday People?
• Common Libraries for Implementing Homomorphic Encryption
• Limitation and Issues
• My Opinion

What is Homomorphic encryption?

Homomorphic encryption is a cryptographic method that allows specific computations on encrypted data without the need for decryption. It converts data into ciphertext, enabling limited mathematical operations while maintaining encryption security. However, its practicality depends on the type of computations required and use cases, as it has certain limitations.

Different types of Homomorphic Encryption

1. Partially Homomorphic Encryption (PHE) algorithms allows only a limited set of mathematical operations (either addition or multiplication) to be performed on encrypted data.
2. Full Homomorphic Encryption (FHE) algorithms supports both addition and multiplication on encrypted data, enabling any arbitrary computation.

Why don’t I use Full Homomorphic Encryption (FHE) everywhere instead of Partially Homomorphic Encryption (PHE)?

FHE is significantly more complex and resource-intensive, making it less practical for many real-world applications due to its high computational overhead. PHE less flexible but generally more efficient, suitable for scenarios where only one type of operation is needed.

Why is Homomorphic Encryption Important to Everyday People?

At first glance, homomorphic encryption might seem like a complex concept, much like blockchain technology, with limited real-world relevance for the average person. However, if widely adopted in conventional data processing and data collection by websites that handle personal information, it could have a profound impact on data security.

In cases of data breaches, the personal information of individuals would remain protected since it remains encrypted at all times, even during processing. Unlike traditional encryption methods, homomorphic encryption allows computations to be performed on encrypted data without the need to decrypt it, thus preserving privacy. Additionally, since different encryption keys can be used for each piece of data, even if a breach occurs, the risk of exposing sensitive information is significantly minimized. This means that our data would be more secure and less vulnerable to theft or misuse, making it a valuable tool for enhancing privacy and protecting our personal information online.

Common Libraries for Implementing Homomorphic Encryption

Several libraries are available for implementing homomorphic encryption in projects:

1. SEAL (Simple Encrypted Arithmetic Library): Developed by Microsoft Research, it implements the BGV and BFV schemes.
2. HElib: A C++ library that implements the BGV scheme with bootstrapping and the CKKS scheme.
3. TFHE: Implements the TFHE (Fast Fully Homomorphic Encryption over the Torus) scheme.
4. Paillier: A library for the Paillier cryptosystem, which is partially homomorphic.
5. HEAAN: A scheme with native support for fixed-point approximate arithmetic.
6. OpenFHE: An open-source library that provides a unified API for various homomorphic encryption schemes.

Limitation and Issues

While homomorphic encryption offers significant benefits, it faces several challenges in practical applications:

1. Reliability and Difficulty in development: The libraries for FHE or PHE are still in their early stages, making development challenging. Developers often face numerous hurdles due to incomplete libraries, insufficient documentation, and limited functionality.
2. Performance overhead: Homomorphic encryption is highly computationally intensive, causing significant slowdowns compared to unencrypted data processing. This limits its suitability for real-time or high-throughput applications.
3. Complexity: Properly implementing homomorphic encryption requires advanced cryptographic expertise, making it difficult for many developers to integrate effectively.
4. Limited operations: Most homomorphic encryption schemes support only a subset of mathematical operations, restricting the types of computations that can be performed on encrypted data.
5. Large ciphertext size: Encrypted data is typically much larger than the original plaintext, leading to increased storage and bandwidth requirements.

My opinion

The field of homomorphic encryption is gaining significant attention, especially as machine learning models begin leveraging encrypted data formats. This technology is well-suited for tasks like ML since it allows operations to be performed on encrypted data without compromising privacy. Homomorphic encryption can also enable the collection of data from a diverse group to analyze demographics without exposing individual identities. This is useful in scenarios such as aggregating employee salary information to provide insights without revealing any single person’s data, or analyzing rental trends in a region.

Despite its numerous potential use cases, homomorphic encryption faces challenges, including the limitations of current algorithms, the substantial computational power and memory requirements, and the need for more user-friendly libraries. As these libraries continue to evolve, reducing complexity and overhead will be crucial for broader adoption.

Whether it’s just a passing trend or a long-term solution remains to be seen, but for now, it’s certainly worth exploring.

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Let us know which library you’d like to learn about next! Stay tuned for an upcoming blog post reviewing OpenFHE.