Achieving p-sensitive k-anonymity via anatomy

Sun, Xiaoxun and Wang, Hua and Li, Jiuyong and Ross, David (2009) Achieving p-sensitive k-anonymity via anatomy. In: ICEBE 2009: IEEE International Conference on e-Business Engineering , 21-23 Oct 2009, Macau, China.

[img]
Preview
PDF (Published Version)
Sun_Wang_Li_Ross_ICEBE_2009_PV.pdf

Download (826Kb)

Abstract

Privacy-preserving data publishing is to protect sensitive information of individuals in published data while the distortion ratio of the data is minimized. One well-studied approach is the K-anonymity model. Recently, several authors have recognized that K-anonymity cannot prevent attribute disclosure. To address this privacy threat, one solution would be to employ P-sensitive K-anonymity, a novel paradigm in relational data privacy, which prevents sensitive attribute disclosure. P-sensitive K-anonymity partitions the data into groups of records such that each group has at least P distinct sensitive values. Existing approaches for achieving P-sensitive K-anonymity are mostly generalization-based. In this paper, we propose a novel permutation-based approach called anatomy to release the quasi-identifier and sensitive values directly in two separate tables. Combined with a grouping mechanism, this approach not only protects privacy, but captures a large amount of correlation in the microdata. We develop a top-down algorithm for computing anatomized tables that obey the P-sensitive K-anonymity privacy requirement, and minimize the error of reconstructing the microdata. Extensive experiments confirm that anatomy allows significantly more effective data analysis than the conventional publication methods based on generalization.


Statistics for USQ ePrint 6322
Statistics for this ePrint Item
Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Published version deposited in accordance with the copyright policy of the publisher. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. Copyright 2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Depositing User: Mr Xiaoxun Sun
Faculty / Department / School: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 14 Aug 2010 08:40
Last Modified: 02 Jul 2013 23:32
Uncontrolled Keywords: privacy-preserving data publishing; K-anonymity model
Fields of Research (FOR2008): 08 Information and Computing Sciences > 0806 Information Systems > 080604 Database Management
08 Information and Computing Sciences > 0803 Computer Software > 080303 Computer System Security
08 Information and Computing Sciences > 0806 Information Systems > 080609 Information Systems Management
Socio-Economic Objective (SEO2008): E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI: doi: 10.1109/ICEBE.2009.34
URI: http://eprints.usq.edu.au/id/eprint/6322

Actions (login required)

View Item Archive Repository Staff Only