# data61/MP-SPDZ

Versatile framework for multi-party computation

Repository: https://github.com/data61/MP-SPDZ
Canonical: https://ross.abutalabs.com/products/mp-spdz
Language: C++
License: NOASSERTION
License Family: other
Topics: mpc, privacy-enhancing-technologies, multi-party-computation, multiparty-computation, secret-sharing, garbled-circuits, secure-computation, threshold-cryptography, secure-multiparty-computation, confidential-computing, secure-multi-party-computation, smpc, privacy-preserving-computing
Last push: 2026-08-17T05:01:07+00:00

## Health v2 (maintenance only)
Score: 85/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 98, release rhythm 60, longevity 100
- inputs: {"age_days": 2884, "days_push": 16, "days_rel": 58, "gap_med": 194, "n_releases_24m": 4}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1178, forks 364 (observed 2026-08-28T04:03:53.077596+00:00)

## What it is
MP-SPDZ is a versatile C++ framework for secure multi-party computation (MPC) supporting many protocols across various security models, including secret sharing, homomorphic encryption, and garbled circuits. It allows benchmarking and running MPC programs written in a Python-like high-level language on Linux and macOS.

## Use cases
- run secure multi-party computation protocols
- benchmark MPC protocols in different security models
- compute on private data across multiple parties without revealing inputs
- prototype privacy-preserving computations with secret sharing or garbled circuits
- experiment with honest-majority and dishonest-majority MPC settings
- evaluate semi-honest vs malicious security tradeoffs

## When to choose
- you need a research-grade MPC framework supporting many protocols and security models
- you want to benchmark or compare secure computation protocols
- you need privacy-preserving joint computation on secret-shared data

## When to avoid
- you need a simple production-ready privacy solution without MPC expertise
- you need a lightweight library for a single specific protocol only
- your platform is neither Linux nor macOS

## Facets
- artifact type: framework
- maturity: active
- function: cryptography, security, benchmarking, privacy
- domain: security, privacy, developer-tools
- platform: cpp, python
- tags: mpc, secure-multi-party-computation, secret-sharing, garbled-circuits, homomorphic-encryption, privacy-preserving-computing, threshold-cryptography, cryptography, linux, macos

## Member repositories
- data61/MP-SPDZ (main) score 85

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:53.077596+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 2026-08-30T06:26:06.054443+00:00, confidence not recorded.
  - readme: https://github.com/data61/MP-SPDZ (fetched 2026-08-28T04:03:53.077596+00:00, sha 44d3e80158fa)
- Data as of 2026-08-30T08:39:29.467469+00:00.