Privacy Design Patterns for AI Systems - Threats and Protections

Explore essential privacy design patterns for AI systems in this 43-minute panel discussion from USENIX's PEPR '24 conference. Gain insights into privacy-by-design principles for ML pipelines, legal obligations for security measures, and technical risks associated with ML algorithms. Examine privacy-preserving machine learning technologies and analyze challenges posed by large language models and generative AI. Learn actionable strategies to enhance privacy in AI/ML practices from industry experts representing Uber, DoorDash, and Google. Moderated by Debra J Farber of The Shifting Privacy Left Podcast, this panel equips participants with knowledge to address privacy breaches and unlawful data processing risks in AI technologies.

PEPR '24 - Panel: Privacy Design Patterns for AI Systems: Threats and Protections