Time and Location: Monday 07/07 at 11am in LC400
Abstract: 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. Based on the model, I will discuss three anonymization algorithms that can ensure privacy protection against the adversary we consider. The effectiveness and efficiency of these algorithms will be demonstrated through experimental results. Finally, I will conclude the talk with my plan for future research.
Bio: Xiaokui Xiao obtained the Bachelor and Master degrees in Computer Science from the South China University of Technology in July 2001 and June 2004, respectively. He is currently a PhD student in the Department of Computer Science and Engineering of the Chinese University of Hong Kong.