Dawn Song

Dawn Song, a UC Berkeley professor specializing in security and machine learning, discusses crucial topics like the vulnerabilities in software and the risks posed by human error. She delves into adversarial machine learning, revealing its implications for autonomous vehicles and the need for enhanced defenses. Privacy concerns and data ownership dynamics are highlighted, alongside emerging strategies like differential privacy. The conversation also touches on program synthesis and the journey from physics to computer science, emphasizing the beauty of both fields.

Award-winning professor and tech entrepreneur Dawn Song joins Jon Krohn to discuss Responsible Decentralized Intelligence. Topics include homomorphic encryption, differential privacy, multi-party computation, PrivateSQL, deep learning, federated learning, and the concept of a responsible data economy.

Dawn Song, a Professor of Computer Science at UC Berkeley and CEO of Oasis Labs, dives into the future of data privacy and security. She discusses creating a responsible data economy that empowers users while leveraging techniques like blockchain and differential privacy. The conversation highlights the risks of adversarial attacks on AI, and how innovations in homomorphic encryption can secure sensitive data. Dawn also shares insights on balancing privacy with effective COVID-19 contact tracing, emphasizing the importance of technology in protecting personal information.