An efficient hash-based algorithm for minimal k-anonymity

Sun, Xiaoxun and Li, Min and Wang, Hua and Plank, Ashley (2008) An efficient hash-based algorithm for minimal k-anonymity. In: ACSC 2008: 31st Australasian Computer Science Conference, 22-25 Jan 2008, Wollongong, Australia.

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Abstract

A number of organizations publish microdata for purposes such as public health and demographic research. Although attributes of microdata that clearly identify individuals, such as name and medical care card number, are generally removed, these databases can sometimes be joined with other public databases on attributes such as Zip code, Gender and Age to re- identify individuals who were supposed to remain anonymous. 'Linking' attacks are made easier by the availability of other complementary databases over the Internet. k-anonymity is a technique that prevents 'linking' attacks by generalizing and/or suppressing portions of the released microdata so that no individual can be uniquely distinguished from a group of size k. In this paper, we investigate a practical model of k- anonymity, called full-domain generalization. We examine the issue of computing minimal k-anonymous table based on the definition of minimality described by Samarati. We introduce the hash-based technique previously used in mining associate rules and present an efficient hash-based algorithm to find the minimal k-anonymous table, which improves the previous binary search algorithm first proposed by Samarati.


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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. Copyright c 2008, Australian Computer Society, Inc. This paper appeared at the Thirty-First Australasian Computer Science Conference (ACSC2008), Wollongong, Australia. Con- ferences in Research and Practice in Information Technology (CRPIT), Vol. 74. Gillian Dobbie and Bernard Mans, Ed. Reproduction for academic, not-for profit purposes permitted provided this text is included.
Depositing User: Mr Xiaoxun Sun
Faculty / Department / School: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 20 Jun 2008 02:27
Last Modified: 02 Jul 2013 23:03
Uncontrolled Keywords: microdata; hash-based algorithm; k-anonymity
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 > 0804 Data Format > 080499 Data Format not elsewhere classified
Socio-Economic Objective (SEO2008): E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
URI: http://eprints.usq.edu.au/id/eprint/4222

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