A subfield of cryptography that enables multiple parties to compute a function while keeping their input data private jointly. MPC allows computation on combined data from multiple sources without revealing the raw data to any parties involved. Some key points about MPC:
- It provides a way to get meaningful results from data analytics without exposing the underlying sensitive data.
- The data remains encrypted and distributed across different parties during the computation.
- No single party can decrypt or view the raw data inputs from other parties.
- Yet the final computed output is the same as if the parties pooled their unencrypted data centrally.
- MPC uses sophisticated cryptographic techniques like secret sharing, oblivious transfer, and garbled circuits.
- Applications include secure business analytics, privacy-preserving machine learning, fraud detection across institutions, etc.
MPC enables collaborative data sharing and processing in an encrypted and privacy-preserving manner, unlocking insights without compromising on data confidentiality. It provides a strong technical control for SaaS providers handling sensitive PII.
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