Safe Machine Learning: Cryptographic Opportunities

This lecture explores the intersection between machine learning and cryptography, focusing on the potential for safe machine learning through cryptographic tools. The instructor delves into the historical context of cryptographic research and its surprising consequences on machine learning. Topics include probabilistically checkable proofs, PAC learning algorithms, learning parity with noise, and the challenges of adversarial machine learning. The lecture emphasizes the importance of privacy, correctness, and security in machine learning models, highlighting the need for cryptographic solutions to address data privacy, model tampering, and adversarial attacks. By leveraging cryptographic techniques developed over the past three decades, the lecture advocates for a new approach to building machine learning models resistant to manipulation and exploitation.