The Confounding Problem of Private Data Release
In our Big Data era, as data-driven decision making sweeps through all aspects of society, the demands to make useful data available are growing ever louder. For example, the ubiquity of GPS-enabled devices has resulted in a wealth of data about the movements of individuals and populations, which can be analyzed for useful information to aid in city and traffic planning, disaster preparedness, and so on. But the problem of releasing such data without disclosing confidential information, such as the places people visit, is a subtle and difficult one. Is “private data release” an oxymoron? This talk will delve into the motivations of private data release, explore the challenges, and outline some of the historical and recent approaches developed in response to this confounding problem.
Divesh Srivastava is the head of Database Research at AT&T Labs-Research. He is an ACM fellow and the managing editor of the Proceedings of the VLDB Endowment (PVLDB). His research interests and publications span a variety of topics in data management. He received his Ph.D. from the University of Wisconsin, Madison, USA, and his Bachelor of Technology from the Indian Institute of Technology, Bombay, India.