Security and Privacy Guarantees in Machine Learning with Differential Privacy
This talk positions differential privacy (DP) -- a theory developed by the privacy community -- as a versatile foundation for building into ML much-needed guarantees of not only privacy but also of security, stability, and transparency. As supporting evidence, I first present PixelDP, a scalable certified defense against adversarial examples that leverages DP theory to guarantee a level of robustness against this attack. I then present Sage, a DP ML platform that bounds the leakage of personal secrets through ML models while addressing some of the most pressing challenges of DP, such as the "running out of privacy budget" problem. Both PixelDP and Sage are designed from a pragmatic systems perspective and illustrate that DP theory is powerful but requires adaptation to achieve practical guarantees for ML workloads.
Roxana Geambasu is an Associate Professor of Computer Science at Columbia University and a member of Columbia's Data Sciences Institute. She joined Columbia in Fall 2011 after finishing her Ph.D. at the University of Washington. For her work in cloud and mobile data privacy, she received: an Alfred P. Sloan Faculty Fellowship, an NSF CAREER award, a Microsoft Research Faculty Fellowship, several Google Faculty awards, a "Brilliant 10" Popular Science nomination, the Honorable Mention for the 2013 inaugural Dennis M. Ritchie Doctoral Dissertation Award, a William Chan Dissertation Award, two best paper awards at top systems conferences, and the first Google Ph.D. Fellowship in Cloud Computing.
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