Homomorphic Encryption: Is it Useful?

TL;DR

Fully Homomorphic Encryption (FHE) lets you do calculations on encrypted data without decrypting it first. It’s amazing, but currently very slow and complex to use in most real-world situations. Progress is being made, especially with new hardware, but widespread adoption isn’t here yet.

What is Homomorphic Encryption?

Normally, if you want to do something with data, you need to decrypt it first. Homomorphic encryption changes that. It allows computations on ciphertext (encrypted data) which produces an encrypted result equivalent to performing the operations on the plaintext (unencrypted data). Think of it like a locked box: you can manipulate things *inside* the box without opening it.

Why is FHE so hard?

1. Complexity: The maths behind FHE is incredibly complex. It involves lattices, polynomials, and noise management.
2. Performance: Operations on encrypted data are *much* slower than operations on regular data – often by a factor of 100x or more. This makes it impractical for many applications today.
3. Size Expansion: Encrypting data with FHE significantly increases its size. A small piece of plaintext can become a very large ciphertext.

Is FHE practical *now*?

For most general-purpose applications, no. However, there are specific use cases where it’s becoming feasible:

1. Privacy-Preserving Machine Learning

• Federated Learning: Training machine learning models on distributed data without revealing the raw data. FHE can help secure individual contributions to the model.
• Encrypted Inference: Performing predictions using a trained model on encrypted input data, protecting user privacy.

2. Secure Cloud Computing

• Outsourcing Calculations: Allowing users to outsource complex calculations to cloud providers without exposing their sensitive data.
• Secure Databases: Performing queries and analysis on encrypted databases.

3. Financial Services

• Privacy-preserving credit scoring: Calculating risk scores without revealing individual financial details.
• Secure auctions: Running auctions where bids remain confidential until the auction closes.

How to get started with FHE (practical steps)

1. Choose a Library: Several libraries are available, each with its strengths and weaknesses.
   • SEAL (Microsoft): A popular C++ library known for performance. https://github.com/microsoft/SEAL
   • HElib: An older, but well-established C++ library. https://github.com/homenc/HElib
   • TFHE: Focuses on boolean circuits and fast operations for specific tasks. https://tfhe.consensys.net/
2. Understand the Basics of Encryption Schemes: FHE isn’t one thing; there are different schemes (e.g., BFV, CKKS). Each has trade-offs in terms of performance and supported operations.
3. Start with Simple Examples: Most libraries provide examples for basic arithmetic operations on encrypted integers or floating-point numbers.

   // Example using SEAL (simplified)

   #include <seal/seal.h>

   int main() {

     seal::EncryptionParameters parms(seal::paillierSchemeType);

     // ... (setup context, key generator, encryptor/decryptor) ...

     seal::Ciphertext encryptedA;

     seal::Ciphertext encryptedB;

     // Encrypt some values...

     seal::Ciphertext encryptedSum = encryptor.encrypt(a + b); // Perform operation on ciphertext

     return 0;

   }

4. Consider Hardware Acceleration: Using GPUs or specialized FHE accelerators can significantly improve performance.
5. Be Aware of Security Considerations: Incorrectly implementing FHE can lead to vulnerabilities. Follow best practices and use well-vetted libraries.

The Future of FHE

Research is rapidly advancing in several areas:

• New Algorithms: Developing more efficient encryption schemes.
• Hardware Acceleration: Creating dedicated hardware for FHE operations.
• Compiler Technology: Automating the process of converting existing code to use FHE.

While full-scale, general-purpose FHE is still some way off, it’s becoming a viable option for specific privacy-critical applications. Keep an eye on this space – it’s evolving quickly!