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learning_from_sensitive_data--_balancing_accuracy_and_privacy

=== Learning from sensitive data– balancing accuracy and privacy ===

Anand Sarwate
Electrical Engineering, UCSD.

Wednesday, March 24th, 2010
2:00pm-3:00pm
EEB 248

Abstract: The advent of electronic databases has made it possible to perform data mining and statistical analyses of populations with applications from public health to infrastructure planning. However, the analysis of individuals' data, even for aggregate statistics, raises questions of privacy which in turn require formal mathematical analysis. A recent measure called differential privacy provides a rigorous statistical privacy guarantee to every individual in the database. We develop privacy-preserving support vector machines (SVM) that gives an improved tradeoff between misclassification error and the privacy level. Our techniques are an application of a more general method for ensuring privacy in convex optimization problems.

Joint work with Kamalika Chaudhuri (UCSD) and Claire Monteleoni (Columbia)

Biography:

Anand Sarwate is currently a Postdoctoral researcher at the Information Theory and Applications Center at the University of California, San Diego. He earned BS degrees in Electrical Engineering and Mathematics from MIT in 2002 and MS and PhD degrees in Electrical Engineering from the University of California, Berkeley in 2005 and 2008, where he was under the supervision of Professor Michael Gastpar. Dr. Sarwate received the Samuel Silver Memorial Scholarship Award and Demetri Angelakos Memorial Achievement Award from the EECS Department at UC Berkeley. His research interests include information theory, distributed signal processing, machine learning, communications, and randomized algorithms for communications and signal processing in sensor networks.

Host: Prof. Alex Dimakis, dimakis [at] usc.edu

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learning_from_sensitive_data--_balancing_accuracy_and_privacy.txt · Last modified: 2016/09/01 19:15 (external edit)