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Practical implementation guide for secure multi-party computation (SMPC): secret sharing, garbled circuits, frameworks (MP-SPDZ, CrypTen), and GDPR-aligned depl
A comprehensive implementation guide that explains core SMPC techniques (Shamir secret sharing, garbled circuits, oblivious transfer), compares frameworks (MP-SPDZ, CrypTen, Sharemind), and outlines GDPR-aligned deployment and governance patterns. It helps engineers design privacy-preserving joint computations (private aggregation, PSI, private ML) and choose appropriate protocols and frameworks for deployment.
Use this skill when implementing or evaluating SMPC for cross-organization analytics, private ML, or any scenario requiring computation over private inputs without centralizing raw data. Appropriate for privacy engineers, researchers, and devops teams planning production or prototype SMPC systems.
Useful for developer-focused/code-capable agents (Codex, Copilot, Claude Code) and any agent that can surface technical docs and run repository scripts.
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