Publishing Microdata with a Robust Privacy Guarantee
Jianneng Cao - Purdue University
Nov 07, 2012Size: 444.5MB
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AbstractToday, the publication of microdata poses a privacy threat. Vast research has striven to define the privacy condition that microdata should satisfy before it is released, and devise algorithms to anonymize the data so as to achieve this condition. Yet, no method proposed to date explicitly bounds the percentage of information an adversary gains after seeing the published data for each sensitive value therein. This paper introduces \beta-likeness, an appropriately robust privacy model for microdata anonymization, along with two anonymization schemes designed therefor, the one based on generalization, and the other based on perturbation. Our model postulates that an adversary's confidence on the likelihood of a certain sensitive-attribute (SA) value should not increase, in relative difference terms, by more than a predefined threshold. Our techniques aim to satisfy a given \beta threshold with little information loss. We experimentally demonstrate that (i) our model provides an effective privacy guarantee in a way that predecessor models cannot, (ii) our generalization scheme is more effective and efficient in its task than methods adapting algorithms for the k-anonymity model, and (iii) our perturbation method outperforms a baseline approach. Moreover, we discuss in detail the resistance of our model and methods to attacks proposed in previous research.
About the SpeakerJianneng is a Postdoctoral Research Associate at the cyber center of Purdue University. He obtained the Ph.D. degree in computer science from National University of Singapore in 2011.
Jianneng's research interests are in data privacy, including data anonymization to hide sensitive personal information and privacy-preserving data mining, as well as access control over streaming data, private record linkage, and query processing on encrypted data.
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