Think Locally Before Computing Jointly: Efficient Secure Multi-Party Computation via Maximizing Local Processing

Secure multi-party computation (MPC) lets parties evaluate a function over private inputs without revealing them. In garbled-circuit (GC) protocols, prior optimization has largely treated the number of non-linear gates, especially AND gates, as a proxy for cryptographic cost, and has focused on minimizing multiplicative complexity. We identify an orthogonal source of savings: a gate need not be garbled when one party knows or can locally compute its inputs; that party can evaluate it outside the secure protocol, even when the gate is non-linear. We introduce ownership-aware optimization for GC-based MPC, which minimizes non-linear gates requiring joint evaluation rather than total non-linear gates. We formalize this objective in a cost model and implement a compiler-integrated, gate-level framework that restructures Boolean circuits to increase party-local computation while preserving secure-computation semantics. The method is automated and general-purpose, and composes with existing multiplicative-complexity optimization. Experiments show that it reduces jointly evaluated computation and improves end-to-end GC performance on circuits with party-local structure, revealing protocol-cost savings missed by conventional GC cost models.