Privacy-enhancing k-anonymization of customer data
|Author:||Sheng Zhong State University of New York, Buffalo|
|Date:||January 12, 2006|
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 entire 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.
Sheng Zhong is an assistant professor in the computer science and engineering department at the State University of New York at Buffalo. He received his PhD in computer science from Yale University in 2004. His research areas include privacy, security, and economic incentives, with applications in wireless networks, data mining and databases. He has published a number papers, some of which appear in prestigous conferences like MOBICOM (No.1 conference in wireless networks), PODS (No.1 conference in database theory), and KDD (No.1 conference in data mining). He is also a PI of the NSF research grant "Incentive-Compatible Protocols."