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Tutorial 1: Differential Privacy in the Wild: A tutorial on current practices and open challenges

Presenters: Ashwin Machanavajjhala (Duke Univ.), Xi He (Duke Univ.), Michael Hay (Colgate University)
Date: Mon Sep 5, 2016
Time: 9.00 a.m. - 10.30 a.m., 11:00 a.m. to 12:30 p.m.
Venue: Pearl 1

Abstract:

Differential privacy has emerged as an important standard for privacy preserving computation over databases containing sensitive information about individuals. Research on differential privacy spanning a number of research areas, including theory, security, database, networks, machine learning, and statistics, over the last decade has resulted in a variety of privacy preserving algorithms for a number of analysis tasks. Despite maturing research efforts, the adoption of differential privacy by practitioners in industry, academia, or government agencies has so far been rare. Hence, in this tutorial, we will first describe the foundations of differentially private algorithm design that cover the state of the art in private computation on tabular data. In the second half of the tutorial we will highlight real world applications on complex data types, and identify research challenges in applying differential privacy to real world applications.

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Bios:

Ashwin Machanavajjhala is an Assistant Professor in the Department of Computer Science, Duke University and an Associate Director at the Information Initiative@Duke (iiD). Previously, he was a Senior Research Scientist in the Knowledge Management group at Yahoo! Research. His primary research interests lie in algorithms for ensuring privacy in statistical databases and augmented reality applications. He is a recipient of the National Science Foundation Faculty Early CAREER award in 2013, and the 2008 ACM SIG MOD Jim Gray Dissertation Award Honorable Mention. Ashwin graduated with a Ph.D. from the Department of Computer Science, Cornell University and a B.Tech in Computer Science and Engineering from the Indian Institute of Technology, Madras. His early work on l-diversity [30] has been very influential in the field of data privacy and has been cited over 2500 times (according to Google Scholar). He also helped design one of the first real data publication powered by formal privacy guarantees in collaboration with the US Census Bureau in 2008 [29]. He has published in PODS, SIGMOD, VLDB, ICDE, WWW and WSDM, and has given tutorials on privacy at IEEE SSP 2009, ICDE 2010, and on entity resolution at AAAI 2012, VLDB 2012 and KDD 2013.

Xi He is a PhD student at Computer Science Department, Duke University. Her research interests lie in privacy-preserving data analysis and security. She has also received an M.S from Duke University and double degree in Applied Mathematics and Computer Science from University of Singapore. Xi has been working with Prof. Machanavajjhala on privacy since 2012, and has published in SIGMOD and VLDB.

Michael Hay is an Assistant Professor in the Department of Computer Science, at Colgate University. Before that he was a Computing Innovation Fellow at Cornell University and completed his PhD at UMass Amherst in 2010. His research interests include privacy-preserving data analysis, data management, data mining, social networks, and privacy. His PhD thesis titled "Enabling Accurate Analysis of Private Network Data" is the recipient of the 2011 ACM SIGKDD Dissertation Award. His ICDM 2009 paper titled "Accurate estimation of the degree distribution of private networks" received the Best Student Paper award. He has given a tutorial on privacy and graphs at SIGMOD 2011.