Secure Multi-Party Computation
A cryptographic protocol where multiple parties jointly compute a function without revealing their respective input data to each other.
SMPC enables joint computations between parties without anyone seeing others' raw data – ideal for privacy-compliant cross-company analytics.
Explanation
Each party holds private data. Through secret sharing or garbled circuits, computation is split so nobody sees complete data. Only the result is revealed.
Marketing Relevance
Enables joint marketing analytics between companies without raw data exchange: conversion matching, cross-company insights, regulated data rooms.
Example
Two ad networks jointly compute conversion attribution without sharing user data. Each only sees the aggregated result, never the other's data.
Common Pitfalls
High communication overhead. Scales poorly with many parties. Complex protocol implementation. Colluding parties can break security.
Origin & History
Andrew Yao introduced the "Millionaires Problem" and Garbled Circuits in 1982. Secret sharing goes back to Shamir (1979). Today Meta (Private Lift), Google (Private Join), and Apple use MPC for privacy-preserving analytics.
Comparisons & Differences
Secure Multi-Party Computation vs. Homomorphic Encryption
HE allows one server to compute on encrypted data; SMPC distributes computation across multiple equal parties.
Secure Multi-Party Computation vs. Federated Learning
Federated Learning trains models in a decentralized way; SMPC enables arbitrary joint computations without data disclosure.