Privacy-enhancing k-anonymization of customer data

In order to protect individuals' privacy, the technique of kanonymization has been proposed to de-associate sensitive attributes from the corresponding identifiers. In this paper, 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 custom...

• Type: Conference
• Year: 2005
• Where: PODS
• Authors: Sheng Zhong, Zhiqiang Yang, Rebecca N. Wright
• Keywords: Corresponding Identifiers, Database, De-associate Sensitive Attributes, PODS 2005, Sensitive Attributes