6.S976 / 18.S996 Cryptography and Machine Learning: Foundations and Frontiers

Cryptography offers a playbook for building trust on untrusted platforms. This course applies that playbook to modern machine learning. We will study how cryptographic modeling and tools—ranging from privacy-preserving algorithms to interactive proofs and debate protocols—can endow ML systems with privacy, verifiability, and reliability. Topics include mechanisms for data and model privacy; methods to verify average-case quality and certify worst-case correctness; and strategies for robustness and alignment across discriminative and generative models. The course will start to draw the contours of a new field at the Crypto × ML interface and identify concrete problems in trustworthy ML that benefit from cryptographic thinking and techniques.