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Secure Multi-Party Computation for Collaborative AI

Zero-knowledge proves a computation and homomorphic encryption computes on one party's ciphertext; secure multi-party computation lets several distrusting parties jointly compute over their combined private data without any one seeing another's inputs. It is the missing third leg of confidential AI. This series builds MPC from secret sharing and garbled circuits through private set intersection to federated learning with secure aggregation. Grounded in Shamir, Yao, and GMW.

Murali Chillakuru·5 episodes
  1. 1
    The MPC Guarantee, Defined: Computing Without Revealing Inputs, and the Adversary Models
    Watch ▸6:57
  2. 2
    Secret Sharing and Threshold Cryptography: Shamir, Additive Shares, and Threshold Decryption
    Watch ▸6:32
  3. 3
    Garbled Circuits and GMW: Two Routes to General Multi-Party Computation and Their Trade-offs
    Watch ▸6:54
  4. 4
    Private Set Intersection and Federated Primitives: The Workhorses of Privacy-Preserving Joins
    Watch ▸6:37
  5. 5
    MPC for Machine Learning: Federated Learning, Secure Aggregation, and the Confidential-Computing Trio
    Watch ▸7:16