Optimizing Linear Counting Queries under Differential Privacy
Chao Li, Michael Hay, Vibhor Rastogi, Gerome Miklau, and Andrew McGregor
Chao Li currently works at google as an engineer on privacy and storage related projects. Before joining google, he was at UMass Amherst as a Ph.D student, advised by Prof Georme Miklau. His research interests including privacy data collection, access, and sharing, especially with industrial applications; computational validation for differential privacy algorithms; and privacy analysis on machine learning models.
Michael Hay is an Associate Professor of Computer Science at Colgate University and co-founder of Tumult Labs, a startup that helps organizations safely release data using differential privacy algorithms. His research interests include data privacy, databases, data mining, machine learning, and social network analysis. From 2017-2019, he was a Research Data Scientist at the US Census Bureau. He received Ph.D. from the University of Massachusetts Amherst in 2010 and completed a Computing Innovation Fellowship at Cornell University from 2010-2012. His research is supported by grants from DARPA and NSF.
Vibhor Rastogi is a Research Scientist Manager at Facebook where he founded the machine learning team for Messenger ads. The team focuses on connecting people and businesses efficiently and safely via ads and chatbots, while removing harmful actors through AI and NLP. Prior to that, Vibhor has worked on machine learning in ads at Twitter and Google.
Gerome Miklau is a Professor of Computer Science at the University of Massachusetts, Amherst. His research focuses on private and secure data management. He designs algorithms to accurately learn from data without disclosing sensitive facts about individuals, primarily in the model of differential privacy. He also designs novel techniques for controlling access to data, limiting retention of data, and resisting forensic analysis. He recently co-founded Tumult Labs, a start-up focused on commercializing privacy technology. Prior to that, he consulted for the U.S. Census Bureau on algorithms that will be deployed for the 2020 decennial census.
Professor Miklau received the Best Paper Award at the International Conference of Database Theory in 2013, the ACM PODS Alberto O. Mendelzon Test-of-Time Award in 2012, a Lilly Teaching Fellowship in 2011, an NSF CAREER Award in 2007, and he won the 2006 ACM SIGMOD Dissertation Award. He received his Ph.D. in Computer Science from the University of Washington in 2005. He
earned Bachelor’s degrees in Mathematics and in Rhetoric from the University of California, Berkeley, in 1995.
Andrew McGregor is an Associate Professor at the University of Massachusetts, Amherst. He received a B.A. degree and the Certificate of Advance Study in Mathematics from the University of Cambridge and a Ph.D. from the University of Pennsylvania. He also spent a couple of years as a post-doc at UC San Diego and Microsoft Research SVC. He is interested in many areas of theoretical computer science and specializes in data stream algorithms and linear sketching. He received the NSF Career Award in 2010 and the College Outstanding Teacher Award in 2016. He currently directs the UMass TRIPODS Institute on the Theoretical Foundations of Data Science.