Title:
The Implications of Privacy-Aware Choice
The Implications of Privacy-Aware Choice
dc.contributor.author | Cummings, Rachel | |
dc.contributor.corporatename | Georgia Institute of Technology. Institute for Information Security & Privacy | en_US |
dc.contributor.corporatename | Georgia Institute of Technology. School of Industrial and Systems Engineering | en_US |
dc.date.accessioned | 2018-11-02T14:56:55Z | |
dc.date.available | 2018-11-02T14:56:55Z | |
dc.date.issued | 2018-10-19 | |
dc.description | Presented on October 19, 2018 at 12:00 p.m. in the Krone Engineered Biosystems Building, Room 1005. | en_US |
dc.description | Dr. Rachel Cummings is an Assistant Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. Her research interests lie primarily in data privacy, with connections to machine learning, algorithmic economics, optimization, statistics, and information theory. | en_US |
dc.description | Runtime: 67:36 minutes | en_US |
dc.description.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. I 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, I 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, I 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 the usefulness of the computation. I will demonstrate that this intuition can fail when strategic concerns affect consumer behavior. Finally, I'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. (based on joint works with Federico Echenique, Ori Heffetz, Katrina Ligett, Mallesh Pai, Aaron Roth, Adam Wierman. Papers available online: https://arxiv.org/abs/1401.0336 and https://arxiv.org/abs/1508.03080) | en_US |
dc.format.extent | 67:36 minutes | |
dc.identifier.uri | http://hdl.handle.net/1853/60513 | |
dc.language.iso | en_US | en_US |
dc.publisher | Georgia Institute of Technology | en_US |
dc.relation.ispartofseries | Cybersecurity Lecture Series | |
dc.subject | Data | en_US |
dc.subject | Legal | en_US |
dc.subject | Privacy | en_US |
dc.title | The Implications of Privacy-Aware Choice | en_US |
dc.type | Moving Image | |
dc.type.genre | Lecture | |
dspace.entity.type | Publication | |
local.contributor.corporatename | School of Cybersecurity and Privacy | |
local.contributor.corporatename | College of Computing | |
local.relation.ispartofseries | Institute for Information Security & Privacy Cybersecurity Lecture Series | |
relation.isOrgUnitOfPublication | f6d1765b-8d68-42f4-97a7-fe5e2e2aefdf | |
relation.isOrgUnitOfPublication | c8892b3c-8db6-4b7b-a33a-1b67f7db2021 | |
relation.isSeriesOfPublication | 2b4a3c7a-f972-4a82-aeaa-818747ae18a7 |
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