Secure Multiparty Computation (SMPC)

Secure multiparty computation (SMPC) is a cryptographic technique that allows multiple parties to jointly compute a function over their combined private inputs without any party revealing its raw data to the others. Each participant holds a secret share or encrypted representation of their data, and the computation is performed in a way—using techniques such as secret sharing, garbled circuits, or homomorphic encryption—that produces a correct output while preserving the privacy of each party's underlying data. SMPC provides strong formal privacy guarantees rooted in cryptographic theory, distinguishing it from softer privacy-preserving approaches that rely on trust in a central party.

In commerce and enterprise AI, SMPC addresses the challenge of collaborative intelligence in contexts where data sharing is legally prohibited, competitively sensitive, or commercially untenable. Competing retailers could jointly train a fraud detection model on their transaction data without exposing individual customer records or proprietary transaction patterns to one another. Financial institutions can compare watchlists for compliance purposes without disclosing their client relationships. Healthcare organizations can collaborate on clinical AI models without violating patient privacy regulations. While SMPC carries meaningful computational overhead that limits its use in latency-sensitive applications, advances in implementation efficiency and growing regulatory pressure around data privacy are making SMPC an increasingly practical and strategically important component of privacy-preserving AI architectures.