data61/MP-SPDZ

Health v2 · maintenance only

Flags: no_license

Adoption not part of the score

1178 stars · 364 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

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

framework · maturity active

cryptography security benchmarking privacy security privacy developer-tools cpp python mpc secure-multi-party-computation secret-sharing garbled-circuits homomorphic-encryption privacy-preserving-computing threshold-cryptography cryptography linux macos

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markdown · JSON · MCP: product_card(name="data61/MP-SPDZ")