An approximate microaggregation approach for microdata protection

[img]
Preview
Expert Systems with Applications.pdf - Accepted Version (161kB) | Preview

Sun, Xiaoxun, Wang, Hua, Li, Jiuyong and Zhang, Yanchun (2012) An approximate microaggregation approach for microdata protection. Expert Systems with Applications, 39 (2). pp. 2211-2219. ISSN 0957-4174 (print), 1873-6793 (online)

Abstract

Microdata protection is a hot topic in the field of Statistical Disclosure Control, which has gained special interest after the disclosure of 658,000 queries by the America Online (AOL) search engine in August 2006. Many algorithms, methods and properties have been proposed to deal with microdata disclosure. One of the emerging concepts in microdata protection is k-anonymity, introduced by Samarati and Sweeney. k-Anonymity provides a simple and efficient approach to protect private individual information and is gaining increasing popularity. k-Anonymity requires that every record in the microdata table released be indistinguishably related to no fewer than k respondents. In this paper, we apply the concept of entropy to propose a distance metric to evaluate the amount of mutual information among records in microdata, and propose a method of constructing dependency tree to find the key attributes, which we then use to process approximate microaggregation. Further, we adopt this new microaggregation technique to study k-anonymity problem, and an efficient algorithm is developed. Experimental results show that the proposed microaggregation technique is efficient and effective in the terms of running time and information loss.

Dimensions Badge

Altmetric Badge

Item type Article
URI https://vuir.vu.edu.au/id/eprint/22136
DOI https://doi.org/10.1016/j.eswa.2011.04.223
Official URL http://www.sciencedirect.com/science/article/pii/S...
Subjects Current > FOR Classification > 0804 Data Format
Current > Division/Research > College of Science and Engineering
Keywords ResPubID25939, privacy protection, data anonymization, anonymisation, anonymity, k-anonymity, data security
Citations in Scopus 8 - View on Scopus
Download/View statistics View download statistics for this item

Search Google Scholar

Repository staff login