Invited Talk 3: Observations at the Intersection of Privacy and Machine Learning

Privacy presents a distinctive set of challenges in machine learning risk management. This talk will explore the intersection of privacy and ML risks, highlighting key barriers to effective risk management. Drawing from recent work at the NIST Privacy Enhancing Technologies (PETs Testbed), the talk will examine the challenges of identifying and mitigating privacy risks in ML systems. Through specific examples from recent red teaming exercises, I will illustrate the complexities of managing privacy risks in ML, including issues related to data minimization, model interpretability, and adversarial attacks. The observations will inform a discussion on the need for a nuanced and multidisciplinary approach to approaching privacy-related ML risks.