Privacy-preserving vector search (Homomorphic encryption)
Privacy-preserving vector search aims to let a server perform nearest-neighbor search over vectors without learning the vectors or the query in the clear. Homomorphic encryption (HE) allows computation on ciphertexts so that results match what would be obtained on plaintexts—in theory, the server could compute distances on encrypted vectors and return encrypted results, with only the client decrypting.
Summary
- Privacy-preserving vector search lets a server perform nearest-neighbor search without learning vectors or the query in the clear. Homomorphic encryption (HE) allows computation on ciphertexts so results match plaintext; in theory the server could compute distances on encrypted vectors and return encrypted results.
- Full HE for high-dimensional ANN is still very expensive. Practical approaches: 2PC/MPC (only client learns top-k); TEEs (enclave); differential privacy (noise, reduced accuracy); encryption at rest + in-transit (server still decrypts to search). See what can be inferred from vectors.
- Pipeline: client encrypts or participates in 2PC; server computes on ciphertext or in TEE. Practical tip: use 2PC/TEE for strong guarantees; encryption at rest is baseline.
Reality and practical approaches
Reality: full HE for high-dimensional vectors and exact or approximate ANN is still very expensive (slow and large ciphertexts). Practical approaches today include: (1) Secure two-party computation (2PC) or secure multi-party computation (MPC)—client and server collaborate so that only the client learns the top-k and optionally the distances; the server never sees plaintext query or full DB vectors. (2) Trusted execution environments (TEEs)—run the VDB or index inside an enclave so the host cannot read data; the threat model is different (hardware trust). (3) Differential privacy—add noise to embeddings or results to limit inference about individuals; this reduces accuracy. (4) Encryption at rest + in-transit—standard practice; the server still decrypts to search, so it is not “search on encrypted data” in the cryptographic sense.
Trade-off: 2PC and TEEs give strong privacy guarantees but add latency and complexity; differential privacy is simpler but reduces accuracy. For most production systems, encryption at rest and in-transit plus access control and understanding what can be inferred from vectors is the starting point.
Research and production
Research continues on HE-friendly distance metrics and approximate algorithms that work on encrypted data with acceptable overhead. For now, production privacy often relies on access control, understanding what can be inferred from vectors, and 2PC/TEE where strong guarantees are required.
Pipeline: client encrypts or participates in 2PC; server computes on ciphertext or in TEE. Practical tip: use 2PC/TEE for strong guarantees; encryption at rest is baseline.
Frequently Asked Questions
What is privacy-preserving vector search?
Letting a server perform nearest-neighbor search over vectors without learning the vectors or the query in the clear. Homomorphic encryption (HE) allows computation on ciphertexts so results match plaintext; the server could compute distances on encrypted vectors and return encrypted results, with only the client decrypting. See nearest-neighbor.
Why is full HE not practical yet?
Full HE for high-dimensional vectors and exact or approximate ANN is still very expensive (slow and large ciphertexts). Research continues on HE-friendly distance metrics and approximate algorithms. For now, production often uses access control, understanding what can be inferred from vectors, and 2PC/TEE where strong guarantees are required.
What are 2PC and TEEs?
Secure two-party computation (2PC) or MPC: client and server collaborate so only the client learns the top-k; the server never sees plaintext query or full DB vectors. Trusted execution environments (TEEs): run the VDB inside an enclave so the host cannot read data; threat model is hardware trust. Multi-tenant isolation complements these.
What about differential privacy?
Add noise to embeddings or results to limit inference about individuals; this reduces accuracy. Combined with encryption at rest and in-transit (standard practice), it can help for some use cases. The server still decrypts to search, so it is not “search on encrypted data” in the cryptographic sense. See privacy concerns and what can be inferred from vectors.