In this talk, we discuss ways that adversarial machine learning can be used to protect or infringe upon the privacy of users. This includes methods for deincentivizing data scraping by creating “unlearnable” data that cannot be used for model training, and methods for manipulating federated learning systems to extract private data.
Tom Goldstein is the Perotto Associate Professor of Computer Science at the University of Maryland. His research lies at the intersection of machine learning and optimization and targets applications in computer vision and signal processing. Before joining the faculty at Maryland, Tom completed his PhD in Mathematics at UCLA, and was a research scientist at Rice University and Stanford University. Of several awards, Goldstein has received SIAM’s DiPrima Prize, a DARPA Young Faculty Award, a JP Morgan Faculty award, and a Sloan Fellowship.