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MIT 6.S976 and 18.S996 Cryptography and Machine Learning (Spring 2026)

mlcrypto.mit.edu · 1,463 words · saved by 2 readers

MIT 6.S976 and 18.S996 (Spring 2026) Cryptography and Machine Learning: Foundations and Frontiers Course Description 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. Prerequisites: 6.1220 (Algorithms) AND 6.390 (Intro to Machine Learning); or equivalent. Alternatively, permission from the instr

MIT 6.S976 and 18.S996 Cryptography and Machine Learning (Spring 2026) MIT 6.S976 and 18.S996 (Spring 2026) Cryptography and Machine Learning: Foundations and Frontiers --> Course Description 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 ver

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