Sheng Zhong, PrivacyEnhancing k-Anonymization of Customer Data

In order to protect individuals' privacy, the technique of k-anonymization has been proposed to de-associate sensitive attributes from the corresponding identifiers. In this work, we provide privacy-enhancing methods for creating k-anonymous tables in a distributed scenario. Specifically, we consider a setting in which there is a set of customers, each of whom has a row of a table, and a miner, who wants to mine the en- tire table. Our objective is to design protocols that allow the miner to obtain a k-anonymous table representing the customer data, in such a way that does not reveal any extra information that can be used to link sensitive attributes to corresponding identifiers, and without requiring a central authority who has access to all the original data. We give two different formulations of this problem, with provably private solutions. Our solutions enhance the privacy of k-anonymization in the distributed scenario by maintaining end-to-end privacy from the original customer data to the final k-anonymous results. About the speaker: Sheng Zhong received his Ph.D in computer science from Yale University in the year of 2004. He holds an assistant professor position at SUNY Buffalo and is currently on leave for postdoctoral research at the Center for Discrete Mathematics and Theoretical Computer Science (DIMACS). His research interests, on the practical side, are security and incentives in data mining, databases, and wireless networks. On the theoretical side, he is interested in cryptography and game theory.