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Computer Science Department Colloquium
9/25/2018 10:30 am
CoRE A 301

Using Differential Privacy with Decentralized Data

Prof. Anand Sarwate, Department of Electrical and Computer Engineering,Rutgers University

Faculty Host: Bill Steiger


Differential privacy has emerged as one of the de-facto standards for measuring privacy risk when performing computations on sensitive data and disseminating the results. Many machine learning algorithms can be made differentially private through the judicious introduction of randomization, usually through noise, within the computation. Algorithms that guarantee differential privacy are randomized, which causes a loss in performance, or utility. Managing the privacy-utility tradeoff becomes easier with more data, but in several applications we are faced with many data holders, each with a smaller data set. Differential privacy can be used as a way to share private access to decentralized data, allowing researchers to perform studies with a much larger sample size. In this talk I will describe this setting, algorithms for differentially private decentralized learning, and potential applications for collaborative research in neuroimaging.


Anand D. Sarwate joined as an Assistant Professor in the Department of Electrical and Computer Engineering at Rutgers, the State University of New Jersey in 2014. He received a B.S. degree in Electrical Science and Engineering and a B.S. degree in Mathematics from MIT in 2002, an M.S. in Electrical Engineering from UC Berkeley in 2005 and a PhD in Electrical Engineering from UC Berkeley in 2008. From 2008-2011 he was a postdoctoral researcher at the Information Theory and Applications Center at UC San Diego and from 2011-2013 he was a Research Assistant Professor at the Toyota Technological Institute at Chicago, a philanthropically endowed academic computer science institute located on the University of Chicago campus. He has been the Online Editor of the IEEE Information Theory Society (2015-) and an Associate Editor for the IEEE Transactions on Signal and Information Processing over Networks (2015-). Prof. Sarwate received the NSF CAREER award in 2015 and the A. Walter Tyson Assistant Professor Award from the Rutgers School of Engineering in 2018. His interests are in information theory, machine learning, and signal processing, with applications to distributed systems, privacy and security, and biomedical research.