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Data Science Seminar: The Implications of Privacy-Aware Choice

14 Feb
Tuesday, 02/14/2017 4:00pm to 5:00pm
Computer Science Building, Room 151
Speaker: Rachel Cummings

Abstract: Privacy concerns are becoming a major obstacle to using data in the way that we want. It's often unclear how current regulations should translate into technology, and the changing legal landscape surrounding privacy can cause valuable data to go unused. In addition, when people know that their current choices may have future consequences, they might modify their behavior to ensure that their data reveal less--or perhaps, more favorable--information about themselves. Given these concerns, how can we continue to make use of potentially sensitive data, while providing satisfactory privacy guarantees to the people whose data we are using? Answering this question requires an understanding of how people reason about their privacy and how privacy concerns affect behavior.

In this talk, we will see how strategic and human aspects of privacy interact with existing tools for data collection and analysis. Rachel will begin by adapting the standard model of consumer choice theory to a setting where consumers are aware of, and have preferences over, the information revealed by their choices. In this model of privacy-aware choice, she will show that little can be inferred about an individual's preferences once we introduce the possibility that she has concerns about privacy, even when her preferences are assumed to satisfy relatively strong structural properties. Next, she will analyze how privacy technologies affect behavior in a simple economic model of data-driven decision making. Intuition suggests that strengthening privacy protections will both increase utility for the individuals providing data and decrease usefulness of the computation. Rachel will demonstrate that this intuition can fail when strategic concerns affect behavior. Finally, she'll discuss ongoing behavioral experiments, designed to empirically measure how people trade off privacy for money, and to test whether human behavior is consistent with theoretical models for the value of privacy.

Bio: Rachel is a Ph.D. candidate in the Computing and Mathematical Sciences department at Caltech, where she is advised by Katrina Ligett. Her work seeks to bridge the gap between theory and practice in the formal study of privacy. This includes problems such as strategic aspects of data generation, incentivizing truthful reporting of data, impacts of privacy policy, human decision-making, and algorithm design. More broadly, she takes a comprehensive approach to addressing real-world privacy challenges, using a diverse toolkit of both theoretical and practical perspectives. She is supported in part by a Simons Award for Graduate Students in Theoretical Computer Science for 2015-2017.


A reception will be held at 3:40pm in the atrium, outside the presentation room.

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