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.

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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.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: © 2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Faculty/School / Institute/Centre: Historic - Faculty of Sciences - Department of Maths and Computing (Up to 30 Jun 2013)
Faculty/School / Institute/Centre: Historic - Faculty of Sciences - Department of Maths and Computing (Up to 30 Jun 2013)
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 (2008): 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
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4605 Data management and data science > 460599 Data management and data science not elsewhere classified
46 INFORMATION AND COMPUTING SCIENCES > 4604 Cybersecurity and privacy > 460499 Cybersecurity and privacy not elsewhere classified
46 INFORMATION AND COMPUTING SCIENCES > 4609 Information systems > 460908 Information systems organisation and management
Socio-Economic Objectives (2008): E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI: https://doi.org/10.1109/ICEBE.2009.34
URI: http://eprints.usq.edu.au/id/eprint/6322

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