Privacy Preserving Data Publication: From Generalization to Anatomy.
Yufei Tao · Conference on Management of Data · 2006
Companies and organizations often need to publish clients' information to institutions for research purposes. For example, a hospital periodically releases patients' diagnostic records so that medical scientists can study the correlation between diseases and various factors. Privacy preservation is an important topic in data publication. First, the publication should be fuzzy enough to disallow any adversary to figure out the exact medical history of any patient. On the other hand, the released data must be sufficiently precise to enable effective analysis. In this tutorial, we will review the existing techniques for striking an appropriate balance, in order to maximize the accuracy of data investigation, without breaching any patient's privacy. Speaker’s Profile: Yufei Tao is the winner of the Hong Kong Young Scientist Award 2002, conferred by the Hong Kong Institution of Science. He holds a PhD degree in computer science from the Hong Kong University of Science and Technology, and did his post-doc as a visiting scientist in the Computer Science department of the Carnegie Mellon University, from 2002 to 2003. In the next three years, he was an assistant professor at the City University of Hong Kong. Currently he is an assistant professor at the Department of Computer Science and Engineering, the Chinese University of Hong Kong. Prof. Tao is engaged in research of database systems. His research interests include temporal databases, spatial databases, approximate query processing, data privacy and security. He has published extensively in renowned conferences and journals including ACM SIGMOD, VLDB, IEEE ICDE, ACM TODS, IEEE TKDE, VLDB JOURNAL, etc. (Duration: 1.5 Hours)