ABSTRACT: In this talk, we will give a friendly introduction to Differential Privacy, a rigorous methodology for analyzing data subject to provable privacy guarantees, that has recently been widely deployed in several settings. The talk will specifically focus on the relationship between differential privacy and machine learning, which is surprisingly rich. This includes both the ability to do machine learning subject to differential privacy, and tools arising from differential privacy that can be used to make learning more reliable and robust (even when privacy is not a concern).

Aaron Roth is the class of 1940 Bicentennial Term Associate Professor of Computer and Information Science at the University of Pennsylvania computer science department, associated with the theory group, and the Warren Center for Network and Data Sciences. He is co-director of the program in Networked and Social Systems Engineering at the University of Pennsylvania and is also affiliated with the AMCS program (Applied Mathematics and Computational Science). He spent a year as a postdoc at Microsoft Research New England. Before that, he received his PhD from Carnegie Mellon University, where he was fortunate to have been advised by Avrim Blum. His main interests are in algorithms, and specifically in the areas of private data analysis, fairness in machine learning, game theory and mechanism design, and learning theory. Aaron is the recipient of a Presidential Early Career Award for Scientists and Engineers (PECASE), an Alfred P. Sloan Research Fellowship, an NSF CAREER award, a Google Faculty Research Award, and a Yahoo Academic Career Enhancement award.