# 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。

Repository: https://github.com/primihub/primihub
Canonical: https://ross.abutalabs.com/products/primihub
Homepage: https://docs.primihub.com/
Language: C++
License: Apache-2.0
License Family: permissive
Topics: federated-learning, private-information-retrieval, private-set-intersection, pir, psi, hacktoberfest, mpc, fl, privacy-preserving, multi-party-computation, security-protocol
Last push: 2025-10-22T02:42:46+00:00

## Health v2 (maintenance only)
Score: 44/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 48, release rhythm 8, longevity 100
- inputs: {"age_days": 1633, "days_push": 315, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1408, forks 194 (observed 2026-08-28T04:04:38.605474+00:00)

## What it is
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
- artifact type: application
- maturity: active
- function: security, cryptography, machine-learning, search-engine, privacy
- domain: privacy, security, machine-learning, self-hosted, data-science
- platform: self-hosted, cpp
- tags: privacy-preserving-computing, secure-multi-party-computation, federated-learning, private-set-intersection, private-information-retrieval, homomorphic-encryption, data-availability, linux, docker

## Member repositories
- primihub/primihub (main) score 44

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:38.605474+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:38:36.431612+00:00, confidence not recorded.
  - readme: https://github.com/primihub/primihub (fetched 2026-08-28T04:04:38.605474+00:00, sha 4cb63165081a)
  - homepage: https://docs.primihub.com/ (fetched 2026-08-29T11:54:19.723887+00:00, sha 0244b53022e7)
  - site_page: https://docs.primihub.com/docs/advance-usage/start/quick-start (fetched 2026-08-29T11:54:19.734640+00:00, sha d77bf8f71491)
  - site_page: https://docs.primihub.com/docs/advance-usage/faq (fetched 2026-08-29T11:54:19.738032+00:00, sha a2fd6164a579)
- Data as of 2026-08-30T08:39:29.467469+00:00.