primihub/primihub
Privacy-Preserving Computing Platform 由密码学专家团队打造的开源隐私计算平台，支持多方安全计算、联邦学习、隐私求交、匿踪查询等。 observed · 2026-08-28
Health v2 · maintenance only
44/100
- Activity 48
- Release rhythm 8
- Longevity 100
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 1633
- days_rel: n/a
- days_push: 315
- n_releases_24m: 0
Adoption not part of the score
1408 stars · 194 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
PrimiHub is an open-source privacy-preserving computing platform built by a team of cryptography experts, supporting secure multi-party computation (MPC), federated learning, private set intersection (PSI), and private information retrieval (PIR). It is deployable via Docker with a CLI, Web UI, and Python SDK for running privacy-preserving tasks.
Use cases
- run private set intersection between two datasets without revealing non-matching records
- perform anonymous query / private information retrieval against a dataset
- train models with federated learning across parties without sharing raw data
- compute joint statistics across organizations while keeping data private
- deploy a self-hosted privacy computing platform with Docker
- explore MPC and PSI protocols hands-on
When to choose
- you need data collaboration between parties where raw data cannot be shared
- you want an open-source, self-hosted alternative to commercial privacy computing platforms
- you need PSI, PIR, federated learning, or MPC out of the box with Docker deployment
- compliance with data protection regulations requires 'data usable but not visible'
When to avoid
- you need a lightweight library to embed in your own app rather than a deployable platform
- your team lacks the 4-core/16GB+ infrastructure the platform expects
- you need production support and certifications of a commercial vendor
- your use case is simple analytics with no privacy constraints
Facets
application · maturity active
security cryptography machine-learning search-engine privacy privacy security machine-learning self-hosted data-science self-hosted cpp privacy-preserving-computing secure-multi-party-computation federated-learning private-set-intersection private-information-retrieval homomorphic-encryption data-availability linux docker
4 sources
- readme: https://github.com/primihub/primihub · fetched 2026-08-28 · 4cb63165081a
- homepage: https://docs.primihub.com/ · fetched 2026-08-29 · 0244b53022e7
- site_page: https://docs.primihub.com/docs/advance-usage/start/quick-start · fetched 2026-08-29 · d77bf8f71491
- site_page: https://docs.primihub.com/docs/advance-usage/faq · fetched 2026-08-29 · a2fd6164a579
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| primihub/primihub | main | 44 |
For agents
markdown · JSON · MCP: product_card(name="primihub/primihub")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem