Privacy-enhancing k-anonymization of customer data

11 years 1 months ago
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...
Sheng Zhong, Zhiqiang Yang, Rebecca N. Wright
Added 08 Dec 2009
Updated 08 Dec 2009
Type Conference
Year 2005
Where PODS
Authors Sheng Zhong, Zhiqiang Yang, Rebecca N. Wright
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