ThesisResearchSecurity 10-14 months
Privacy-Preserving Recommender System using Homomorphic Encryption
A recommendation system where the server computes personalized recommendations on encrypted user preference data using homomorphic encryption so user privacy is mathematically guaranteed even if the server is compromised.
PythonTenSEAL (CKKS scheme)PyTorchFastAPINumPy
Find GitHub repos & source code →How to build it step by step
- 1HE Basics: Study CKKS scheme for approximate arithmetic on encrypted vectors; implement basic add/multiply on ciphertexts
- 2Matrix Factorization: Implement encrypted SVD/ALS for collaborative filtering; all dot products computed in ciphertext domain
- 3Protocol Design: Client encrypts preference vector; sends to server; server computes recommendations on ciphertext; returns encrypted result
- 4Performance: Measure recommendation quality (NDCG, MAP) vs plaintext baseline; measure encryption overhead
Key features to implement
- ✓Mathematically proven privacy server cannot see user data
- ✓Accurate recommendations despite encryption
- ✓Client-side key management
- ✓Performance analysis vs plaintext systems
- ✓Practical deployment guide for HE recommenders
💡 Unique twist to stand out
Compare CKKS homomorphic encryption with Secure Multi-Party Computation (SMPC) approaches for the same recommendation task providing a practical guide on when to prefer HE vs SMPC for privacy-preserving ML.
🎓 What you'll learn
Homomorphic encryption theory and practice, privacy-preserving machine learning, recommender system design, and cryptographic protocol engineering.