Vector DB and ANN vs PHE conflict, is there a practical workaround? [D]

2026-04-30 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy, Data Science & Analytics · Depth: Advanced, quick

Summary

A technical discussion explores the fundamental conflict between using vector databases with Approximate Nearest Neighbor (ANN) search algorithms like HNSW or IVF for fast similarity searches and implementing Partially Homomorphic Encryption (PHE) for privacy-preserving embeddings. Encrypted embeddings necessitate linear scans or exact computations, rendering ANN inefficient. A proposed workaround involves storing embeddings as BLOBs in a standard database and using metadata-based filtering (e.g., RFID, tags) to reduce the search space before performing similarity computations on a smaller subset. Key concerns include the scalability of this approach to millions of embeddings, its performance compared to ANN, and whether it merely re-invents a less efficient vector database. The discussion seeks practical solutions for combining ANN with encrypted embeddings, exploring hybrid approaches like secure enclaves or tiered search, and identifying real-world systems achieving privacy-preserving vector search at scale, with a target scale of over 1 million embeddings.

Key takeaway

For AI Scientists and Research Scientists designing privacy-preserving retrieval systems, you must recognize that directly combining ANN with PHE is impractical due to computational overhead. Your focus should shift to hybrid architectures that either pre-filter search spaces using unencrypted metadata or explore secure enclaves and partial decryption to balance privacy and performance for large-scale embedding retrieval. Evaluate the trade-offs between data trust models and the complexity of cryptographic solutions.

Key insights

PHE for embeddings fundamentally conflicts with ANN search efficiency, requiring alternative privacy-preserving search strategies.

Principles

• Encrypted embeddings disable ANN.
• Metadata filtering can reduce search space.
• Trust assumptions dictate encryption needs.

Method

Store encrypted embeddings as BLOBs in a standard database, then use metadata (RFID/tags) to filter candidates before performing exact similarity computations on the reduced subset.

In practice

• Consider pynndescent for custom distance metrics.
• Evaluate metadata filtering for scalability.
• Assess secure enclaves for hybrid solutions.

Topics

• Vector Databases
• Approximate Nearest Neighbor
• Partially Homomorphic Encryption
• Privacy-Preserving Search
• Embedding Search

Best for: AI Scientist, Research Scientist, AI Engineer, Machine Learning Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.