Show HN: Homomorphically Encrypted Vector Database
github.comAs personal AI agents like OpenClaw become more powerful by leveraging intimate user data, privacy has emerged as a fundamental bottleneck.
We’re releasing HEVEC, a vector database built on homomorphic encryption, enabling end-to-end privacy with real-time search at scale.
HEVEC is designed as a drop-in alternative to plaintext vector databases and supports real-time encrypted search at scale (1M vectors in ~187 ms).
Key points: - A secure, drop-in alternative to plaintext vector databases - End-to-end homomorphic encryption for both data and queries - Real-time encrypted search at scale (1M vectors in 187 ms)
As personal AI agents become deeply personalized, data ownership must belong to users.
HEVEC enforces this through privacy-by-design architecture.
We’d appreciate feedback from the AI, systems, and privacy communities.
This is very interesting, thank you for sharing. I haven't been doing FHE for a few years and I'm sure things are progressing rapidly. The last I was toying around there were some blockchains that were trying to allow for distributed computation for trusted computing. The overall outcome was that it wasn't ready and I was going to wait a bit to try again.
Is this closer to Fully Homomorphic Encryption (FHE) or partial?
Thanks for the interest.
HEVEC uses partial homomorphic encryption, not FHE.
It supports only the operations needed for an encrypted vector database (search, insert, delete, etc.), which keeps performance practical.
Implementation details are in our paper: https://arxiv.org/abs/2506.17336
Happy to elaborate if helpful.
Are you guys currently providing this as a SaaS or hostable solution? I am interested in standing it up and offering it, but I think it's important to know if anyone else is already working on that and in what markets.