PrivacyEnhancing k-Anonymization of Customer Data

Apr 27, 2005

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Abstract

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.