What is Secure Multi-Party Computation?
A cryptographic technique that allows multiple parties to jointly compute a function over their combined data without revealing their individual inputs to each other.
MPC enables collaboration on sensitive data without anyone sharing their actual data.
How It Works
- Each party holds private input data
- Through cryptographic protocols, they jointly compute a result
- Each party learns only the final output, not anyone else's input
- No trusted third party is needed
Examples
- Salary comparison: Determine who earns more without revealing either salary
- Private auctions: Find the highest bid without revealing any bids
- Medical research: Compute statistics across hospital databases without sharing patient records
- Private voting: Tally votes without revealing individual ballots
Real-World Uses
- Privacy-preserving analytics: Competing companies computing market statistics
- Financial compliance: Cross-institution fraud detection without sharing customer data
- Cryptocurrency: Threshold signatures for multi-party wallets
Trade-offs
MPC is computationally expensive and requires communication between parties. It's practical for many use cases but not yet suitable for high-frequency or very large-scale computations.
Related Terms
Differential Privacy
A mathematical framework for sharing aggregate information about a dataset while provably protecting the privacy of individual entries.
Homomorphic Encryption
A form of encryption that allows computations to be performed on encrypted data without decrypting it first, preserving privacy during processing.
Zero-Knowledge Proof
A cryptographic method by which one party can prove to another party that they know a value, without conveying any information apart from the fact that they know the value. This allows authentication and verification without exposing sensitive data.
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