Satisfying privacy requirements before data anonymization

Sun, Xiaoxun and Wang, Hua and Li, Jiuyong and Zhang, Yanchun (2012) Satisfying privacy requirements before data anonymization. The Computer Journal, 55 (4). pp. 422-437. ISSN 0010-4620

Abstract

In this paper, we study a problem of protecting privacy of individuals in large public survey rating data. We propose a novel (k,ϵ, l)-anonymity model to protect privacy in large survey rating data, in which each survey record is required to be similar to at least k−1 other records based on the non-sensitive ratings, where the similarity is controlled by ϵ, and the standard deviation of sensitive ratings is at least l. We study an interesting yet non-trivial satisfaction problem of the proposed model, which is to decide whether a survey rating data set satisfies the privacy requirements given by the user. For this problem, we investigate its inherent properties theoretically, and devise a novel slicing technique to solve it. We analyze the computation complexity of the proposed slicing technique and conduct extensive experiments on two real-life data sets, and the results show that the slicing technique is fast and scalable with data size and much more efficient in terms of execution time and space overhead than the heuristic pairwise method.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: © 2011 The Author. Permanent restricted access to Published version in accordance with the copyright policy of the publisher.(Oxford University Press)
Faculty / Department / School: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 12 Dec 2012 09:44
Last Modified: 12 Feb 2015 05:28
Uncontrolled Keywords: privacy; system security; data anonymization
Fields of Research : 01 Mathematical Sciences > 0103 Numerical and Computational Mathematics > 010301 Numerical Analysis
08 Information and Computing Sciences > 0803 Computer Software > 080303 Computer System Security
08 Information and Computing Sciences > 0804 Data Format > 080499 Data Format not elsewhere classified
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI: 10.1093/comjnl/bxr028
URI: http://eprints.usq.edu.au/id/eprint/21974

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