# YeeZTech/YeeZ-Privacy-Computing

Fidelius - YeeZ Privacy Computing 基于可信执行环境的熠智隐私计算中间件

Repository: https://github.com/YeeZTech/YeeZ-Privacy-Computing
Canonical: https://ross.abutalabs.com/products/yeez-privacy-computing
Homepage: http://yeez.tech
Language: C
License: GPL-3.0
License Family: copyleft
Topics: ai, privacy-protection, sgx, sgx-enclave, tee
Last push: 2026-05-16T14:53:26+00:00

## Health v2 (maintenance only)
Score: 69/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 82, release rhythm 35, longevity 100
- inputs: {"age_days": 1990, "days_push": 109, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1057, forks 137 (observed 2026-08-28T04:03:24.711736+00:00)

## What it is
Fidelius is a privacy computing middleware built on Intel SGX trusted execution environments, enabling secure data collaboration where original data never leaves the data provider's enclave. It supports blockchain networks as trusted third parties for data transmission and computation verification.

## Use cases
- compute on private data without exposing it
- secure multi-party data collaboration
- verify computation results on sensitive data
- run privacy-preserving analytics inside SGX enclaves
- use blockchain as a trusted channel for data exchange
- protect data availability while keeping it invisible

## When to choose
- you need TEE-based privacy computing with Intel SGX hardware
- data providers must never release raw data to data users
- you want blockchain-backed verifiability of collaborative computations

## When to avoid
- you lack SGX-capable CPUs and cannot use the debug mode
- you need a pure software MPC or homomorphic encryption solution
- you require a managed cloud service rather than self-hosted middleware

## Facets
- artifact type: library
- maturity: active
- function: security, cryptography, middleware, machine-learning, blockchain
- domain: privacy, security, blockchain, artificial-intelligence
- platform: cpp, self-hosted
- tags: privacy-computing, trusted-execution-environment, sgx, secure-enclave, data-collaboration, confidential-computing, fidelius, data-engineering, linux

## Member repositories
- YeeZTech/YeeZ-Privacy-Computing (main) score 69

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:24.711736+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 2026-08-30T06:57:55.209389+00:00, confidence not recorded.
  - readme: https://github.com/YeeZTech/YeeZ-Privacy-Computing (fetched 2026-08-28T04:03:24.711736+00:00, sha d327b105a594)
  - homepage: http://yeez.tech (fetched 2026-08-29T12:59:40.023769+00:00, sha 1235175d3258)
  - site_page: https://yeez.tech/about (fetched 2026-08-29T12:59:40.033436+00:00, sha c96da25a673c)
- Data as of 2026-08-30T08:39:29.467469+00:00.