secure multi-party computation

**SMPC** (Secure Multi-Party Computation) is a **cryptographic protocol that enables multiple parties to jointly compute a function on their private inputs without revealing those inputs to each other** — allowing collaborative ML training or inference without exposing any party's sensitive data. **SMPC for ML** - **Secret Sharing**: Split each value into shares distributed across parties — no single party can reconstruct the value. - **Garbled Circuits**: Transform the computation into encrypted boolean circuits that parties evaluate without seeing intermediate values. - **Oblivious Transfer**: One party selects a value from another party's inputs without revealing which value was selected. - **Inference**: Run neural network inference on encrypted data — the model owner doesn't see the data, the data owner doesn't see the model. **Why It Matters** - **Privacy**: Multiple fabs can jointly train a model on their combined data without sharing proprietary process data. - **Correctness**: SMPC guarantees correct computation — the result is the same as if all data were pooled. - **Overhead**: SMPC is computationally expensive — 100-1000× slowdown compared to plaintext computation. **SMPC** is **computing on private data together** — enabling collaborative ML without any party revealing their sensitive data.