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Conservative or liberal? : personalized differential privacy
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Jorgensen, Zach, Yu, Ting and Cormode, Graham (2015) Conservative or liberal? : personalized differential privacy. In: 31st IEEE International Conference on Data Engineering (2015), Seoul, South Korea, 13-17 Apr 2015. Published in: 2015 IEEE 31st International Conference on Data Engineering pp. 1023-1034. ISBN 9781479979646. ISSN 1063-6382. doi:10.1109/ICDE.2015.7113353
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Official URL: http://dx.doi.org/10.1109/ICDE.2015.7113353
Abstract
Differential privacy is widely accepted as a powerful framework for providing strong, formal privacy guarantees for aggregate data analysis. A limitation of the model is that the same level of privacy protection is afforded for all individuals. However, it is common that the data subjects have quite different expectations regarding the acceptable level of privacy for their data. Consequently, differential privacy may lead to insufficient privacy protection for some users, while over-protecting others. We argue that by accepting that not all users require the same level of privacy, a higher level of utility can often be attained by not providing excess privacy to those who do not want it. We propose a new privacy definition called personalized differential privacy (PDP), a generalization of differential privacy in which users specify a personal privacy requirement for their data. We then introduce several novel mechanisms for achieving PDP. Our primary mechanism is a general one that automatically converts any existing differentially private algorithm into one that satisfies PDP. We also present a more direct approach for achieving PDP, inspired by the well-known exponential mechanism. We demonstrate our framework through extensive experiments on real and synthetic data.
| Item Type: | Conference Item (Paper) | ||||||
|---|---|---|---|---|---|---|---|
| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software | ||||||
| Divisions: | Faculty of Science > Computer Science | ||||||
| Library of Congress Subject Headings (LCSH): | Privacy | ||||||
| Series Name: | International Conference on Data Engineering | ||||||
| Journal or Publication Title: | 2015 IEEE 31st International Conference on Data Engineering | ||||||
| Publisher: | IEEE | ||||||
| ISBN: | 9781479979646 | ||||||
| ISSN: | 1063-6382 | ||||||
| Official Date: | 1 June 2015 | ||||||
| Dates: |
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| Page Range: | pp. 1023-1034 | ||||||
| DOI: | 10.1109/ICDE.2015.7113353 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||
| Funder: | European Commission (EC) | ||||||
| Grant number: | PCIG13-GA-2013-618202 (EC) | ||||||
| Conference Paper Type: | Paper | ||||||
| Title of Event: | 31st IEEE International Conference on Data Engineering (2015) | ||||||
| Type of Event: | Conference | ||||||
| Location of Event: | Seoul, South Korea | ||||||
| Date(s) of Event: | 13-17 Apr 2015 | ||||||
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