Article

Privacy-enhancing k-anonymization of customer data

Authors:

Rebecca N. WrightAuthors Info & Claims

PODS '05: Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems

Pages 139 - 147

https://doi.org/10.1145/1065167.1065185

Published: 13 June 2005 Publication History

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 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 customer data to the final k-anonymous results.

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cover image ACM Conferences

PODS '05: Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems

June 2005

388 pages

ISBN:1595930620

DOI:10.1145/1065167

General Chair:
Georg Gottlob
Vienna University of Technology, Austria
,
Program Chair:
Foto Afrati
National Technical University of Athens, Greece

Copyright © 2005 ACM.

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Published: 13 June 2005

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SIGMOD/PODS05: International Conference on Management of Data and Symposium on Principles Database and Systems

June 13 - 15, 2005

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Overall Acceptance Rate 642 of 2,707 submissions, 24%

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Cambiaso ENarteni SBaiardini IBraido FPaglialonga AMongelli M(2024)Advancements on IoT and AI applied to PneumologyMicroprocessors and Microsystems10.1016/j.micpro.2024.105062108(105062)Online publication date: Jul-2024
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Tassa THorin A(2022)Privacy-preserving Collaborative Filtering by Distributed MediationACM Transactions on Intelligent Systems and Technology10.1145/354295013:6(1-26)Online publication date: 22-Sep-2022
https://dl.acm.org/doi/10.1145/3542950
Dhinakaran DPrathap P(2022)Protection of data privacy from vulnerability using two-fish technique with Apriori algorithm in data miningThe Journal of Supercomputing10.1007/s11227-022-04517-078:16(17559-17593)Online publication date: 1-Nov-2022
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Chourey NSoni R(2022)Privacy Preservation Technique Based on Sensitivity Levels for Multiple Numerical Sensitive Overlapped AttributesHybrid Intelligent Systems10.1007/978-3-030-96305-7_5(38-55)Online publication date: 4-Mar-2022
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