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Department of Computer Science Seminar
2008 Series

Transparent Anonymization: Thwarting Adversaries who know the Algorithm

Mr. Xiaokui Xiao
The Chinese University of Hong Kong

Date: April 25, 2008 (Friday)
Time: 4:00 - 5:00 pm
Venue: FSC1217, Fong Shu Chuen Library, Ho Sin Hang Campus

The digitization of our daily lives has led to unprecedented collections of sensitive personal data (e.g., census data, medical records) by governments and corporations. Such data is often released for research purposes, which, however, may pose a risk to individual privacy. To address this issue, numerous techniques have been proposed to anonymize the data before its publication. Somewhat surprisingly, all existing anonymization techniques assume that the adversary has no or limited knowledge of the anonymization algorithm, and fail to protect privacy when this assumption does not hold. In other words, a data publisher that adopts these techniques must take up the difficult responsibility of keeping the algorithm confidential, which severely limits the applicability of these techniques in practice. In this talk, I will present a solution that remedies the above problem. I will start from an analytical model for evaluating disclosure risks, against an adversary who knows everything in the anonymization process, except the data to be published. This model leads to a privacy principle, transparent l-diversity, which ensures privacy protection against the adversary we consider. I will discuss three algorithms that achieve transparent l-diversity, and demonstrate their effectiveness and efficiency with experimental results.

Xiaokui Xiao is a PhD candidate in the Computer Science and Engineering Department of the Chinese University of Hong Kong. His research interests lie in the area of database management, with a focus on data privacy and spatial databases. More information about his research can be found at

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Department of Computer Science, Hong Kong Baptist University
Hong Kong Baptist University