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The Strange Case of Privacy in Equilibrium Models

Cummings, Rachel and Ligett, Katrina and Pai, Mallesh M. and Roth, Aaron (2016) The Strange Case of Privacy in Equilibrium Models. In: Proceedings of the 2016 ACM Conference on Economics and Computation. Association for Computer Machinery , New York, NY, p. 659. ISBN 978-1-4503-3936-0.

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We study how privacy technologies affect user and advertiser behavior in a simple economic model of targeted advertising. In our model, a consumer first decides whether or not to buy a good, and then an advertiser chooses an advertisement to show the consumer. The consumer's value for the good is correlated with her type, which determines which ad the advertiser would prefer to show to her---and hence, the advertiser would like to use information about the consumer's purchase decision to target the ad that he shows. In our model, the advertiser is given only a differentially private signal about the consumer's behavior---which can range from no signal at all to a perfect signal, as we vary the differential privacy parameter. This allows us to study equilibrium behavior as a function of the level of privacy provided to the consumer. We show that this behavior can be highly counter-intuitive, and that the effect of adding privacy in equilibrium can be completely different from what we would expect if we ignored equilibrium incentives. Specifically, we show that increasing the level of privacy can actually increase the amount of information about the consumer's type contained in the signal the advertiser receives, lead to decreased utility for the consumer, and increased profit for the advertiser, and that generally these quantities can be non-monotonic and even discontinuous in the privacy level of the signal.

Item Type:Book Section
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URLURL TypeDescription DOIArticle Paper
Ligett, Katrina0000-0003-2780-6656
Additional Information:© 2016 ACM. A full version of this paper is available online at The first author was supported in part by a Simons Award for Graduate Students in Theoretical Computer Science, NSF grant CNS-1254169, and US-Israel Binational Science Foundation grant 2012348. The second author was supported in part by NSF grants CNS-1254169 and CNS-1518941, DARPA-BAA-15-29, US-Israel Binational Science Foundation grant 2012348, the Charles Lee Powell Foundation, a Google Faculty Research Award, an Okawa Foundation Research Grant, a Microsoft Faculty Fellowship, a subcontract through the DARPA Brandeis project, a grant from the HUJI Cyber Security Research Center, and a startup grant from Hebrew University’s School of Computer Science. The third author was supported in part by NSF Grant CCF-1101389. The fourth author was supported in part by NSF Grant CCF-1101389, an NSF CAREER award, DARPA-BAA-15-29, and an Alfred P. Sloan Foundation Fellowship. Part of this work was completed during a stay at the Simons Institute for the Theory of Computing at Berkeley.
Funding AgencyGrant Number
Simons FoundationUNSPECIFIED
Binational Science Foundation (USA-Israel)2012348
Defense Advanced Research Projects Agency (DARPA)DARPA-BAA-15-29
Charles Lee Powell FoundationUNSPECIFIED
Okawa FoundationUNSPECIFIED
Microsoft Faculty FellowshipUNSPECIFIED
HUJI Cyber Security Research CenterUNSPECIFIED
Hebrew UniversityUNSPECIFIED
Alfred P. Sloan FoundationUNSPECIFIED
Record Number:CaltechAUTHORS:20161117-133623412
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:72134
Deposited By: Tony Diaz
Deposited On:17 Nov 2016 22:29
Last Modified:17 Nov 2016 22:29

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