Please note: This distinguished lecture will take place in DC 1302 and online.
Adam Smith, Professor
Departments of Computer Science and Electrical and Computer Engineering
Boston University
The results of learning and statistical inference reveal information about the data they use. This talk discusses the possibilities and limitations of fitting machine learning and statistical models while protecting the privacy of individual records.
I will begin by explaining what makes this problem difficult, using recent examples of training-data memorization and other breaches. I will present differential privacy, a rigorous definition of privacy in statistical databases that is now widely studied, and increasingly used to analyze and design deployed systems.
Time permitting, I will also present recent algorithmic results on a fundamental problem: differentially private mean estimation. We give an efficient and (nearly) sample-optimal algorithm for estimating the mean of “nicely” distributed data sets. When the data come from a Gaussian or sub-Gaussian distribution, the new algorithm matches the sample complexity of the best nonprivate algorithm.
Bio: Adam Smith is a professor of computer science at Boston University. From 2007 to 2017, he served on the faculty of the Computer Science and Engineering Department at Penn State. His research interests lie in data privacy and cryptography, and their connections to machine learning, statistics, information theory, and quantum computing.
He obtained his Ph.D. from MIT in 2004 and has held postdoc and visiting positions at the Weizmann Institute of Science, UCLA, Boston University and Harvard. His work received a Presidential Early Career Award for Scientists and Engineers (PECASE) in 2009; a Theory of Cryptography Test of Time award in 2016; the Eurocrypt 2019 Test of Time award; the 2017 Gödel Prize; and the 2021 Kanellakis Theory and Practice Award. He is a Fellow of the ACM.