XTrace
Encrypted vector DB (search embeddings without exposing them)
Details
- External ID
- 47867151
- Source
- HN
- Company
- —
- Product
- XTrace
- Website domain
- github.com
- Launched
- April 22, 2026
- Cohort
- —
- Upvotes
- 13
- Upvotes percentile
- 0.6658097686375322
- Tags
- —
- Fetched at
- Sept. 7, 2026, 9:26 p.m.
- Updated at
- Sept. 7, 2026, 9:26 p.m.
Description
Hey everyone! This is XTrace. Wanted to share what we’ve been working on for the past year.We built a private vector database from the ground up that performs similarity search on encrypted vectors. The server never sees your plaintext embeddings or documents.The problem we’re trying to solve: every vector DB today requires plaintext on the server. If you're doing RAG over sensitive data (medical, legal, financial), your embeddings — which researchers have shown can be inverted to recover original text — sit exposed on someone else's infrastructure.XTrace encrypts everything on your machine first. Vectors get Paillier homomorphic encryption, text gets AES-256. The server stores and searches only ciphertexts. Your keys never leave your environment.We just open-sourced the SDK (Apache 2.0). You can run the encryption verification tests offline without even creating an account.Trade-offs we're upfront about: there's latency overhead from the encryption operations. We're actively optimizing this. The free tier is rate-limited but fully functional.Happy to answer questions about the crypto approach, architecture decisions, or anything else.
Enrichment
- Theme
- self-hosted infrastructure and security tools
- Vertical
- Horizontal
- Function
- Data infrastructure
- Audience
- Developer
- AI stance
- AI feature
- Project type
- Commercial product
- Normalized one-liner
- encrypted vector database for embeddings
- Manually corrected
- False
Could you build this?
No Building an encrypted vector database that queries high-dimensional vectors without decrypting them requires cutting-edge cryptographic knowledge in homomorphic encryption (FHE) or secure multi-party computation.
What it would actually take: The system requires an implementation using advanced cryptographic schemes such as CKKS or BFV fully homomorphic encryption, or specialized hardware secure enclaves (AWS Nitro Enclaves / Intel SGX). The hard part is designing approximate nearest neighbor (ANN) search algorithms that can evaluate dot products and Euclidean distances over ciphertexts without prohibitive computational latency and ciphertext explosion, requiring a team of applied cryptographers and systems engineers.
Discussion
3 comments analyzed.
Concerns raised: Data still goes through AI tools even if encrypted locally, Unclear if solution prevents AI companies from retaining conversations
Competitors
Other products that read as similar to this one — 57 launches clear the similarity bar, closest 8 shown.
Attention rank: #20 of 58 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).
Launched 174 days after the earliest competitor.
- SatoriDB · hn · 2025-12-30 · 5 upvotes · similarity 0.44
- Hidetext.sh · hn · 2026-07-11 · 7 upvotes · similarity 0.41
- VectorDBZ, a desktop GUI for vector databases · hn · 2026-01-01 · 13 upvotes · similarity 0.39
- Nullsec · hn · 2026-09-17 · 6 upvotes · similarity 0.39
- VectorFreed · github · 2026-09-21 · 8 upvotes · similarity 0.39
- Unicode Steganography · hn · 2026-04-07 · 59 upvotes · similarity 0.38
- UniDoc Encryption · ph · 2026-09-29 · 1 upvotes · similarity 0.38
- EdgeVec · hn · 2025-12-12 · 7 upvotes · similarity 0.38
Other launches for this product
Same idea, different domain
Nobody's really built a data infrastructure tool for Media & entertainment yet.