Volume 6 Number 10 (Oct. 2011)
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JSW 2011 Vol.6(10): 1937-1944 ISSN: 1796-217X
doi: 10.4304/jsw.6.10.1937-1944

Multi-dimensional K-anonymity based on Mapping for Protecting Privacy

Qian Wang, Cong Xu, Min Sun

College of Computer Science, Chongqing University, Chongqing, China

Abstract—Data release has privacy disclosure risk if not taking any protection policy. Although attributes that clearly identify individuals, such as Name, Identity Number, are generally removed or decrypted, attackers can still link these databases with other released database on attributes (Quasi-identifiers) to re-identify individual’s private information. K-anonymity is a significant method for privacy protection in microdata release. However, it is a NPhard problem for optimal k-anonymity on dataset with multiple attributes. Most partitions in k-anonymity at present are single-dimensional. Research on k-anonymity focuses on getting high quality anonymity while reducing the time complexity. This paper proposes a new multidimensional k-anonymity algorithm based on mapping and divide-and-conquer strategy. Multi-dimensional data are mapped to single-dimensional, and then k-anonymity on multiple attributes is implemented employing the divideand- conquer strategy in polynomial time. Divided dimension selection is prioritized based on information dependency, which significantly reduces the information loss. The experiment shows that the proposed algorithm is feasible and performs much better in k-anonymity.

Index Terms—privacy protection, k-anonymity, multidimension, mapping, partitioning

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Cite: Qian Wang, Cong Xu, Min Sun, "Multi-dimensional K-anonymity based on Mapping for Protecting Privacy," Journal of Software vol. 6, no. 10, pp. 1937-1944, 2011.

General Information

ISSN: 1796-217X (Online)
Frequency:  Quarterly
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, CNKIGoogle Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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