NDLI: Privacy-enhancing k-anonymization of customer data (2005)

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Privacy-enhancing k-anonymization of customer data (2005)

Content Provider	CiteSeerX
Author	Zhong, Sheng Wright, Zhiqiang Yang Rebecca N.
Description	In order to protect individuals ’ privacy, the technique of k-anonymization has been proposed to de-associate sensitive at-tributes 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 en-tire table. Our objective is to design protocols that allow the miner to obtain a k-anonymous table representing the cus-tomer data, in such a way that does not reveal any extra in-formation that can be used to link sensitive attributes to cor-responding identifiers, and without requiring a central author-ity who has access to all the original data. We give two differ-ent formulations of this problem, with provably private solu-tions. 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 re-sults. 1.
File Format	PDF
Language	English
Publisher	ACM Press
Publisher Date	2005-01-01
Publisher Institution	in Proceedings of the 24rd ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems (PODS
Access Restriction	Open
Subject Keyword	Customer Data Cor-responding Identifier De-associate Sensitive At-tributes Sensitive Attribute Extra In-formation Original Customer Data Individual Privacy Privacy-enhancing Method Final K-anonymous Re-sults End-to-end Privacy En-tire Table Provably Private Solu-tions Cus-tomer Data Corresponding Identifier Central Author-ity Distributed Scenario Privacy-enhancing K-anonymization Original Data K-anonymous Table Differ-ent Formulation
Content Type	Text
Resource Type	Article

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