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Frontiers of Computer Science

Front. Comput. Sci.    2018, Vol. 12 Issue (6) : 1241-1254     https://doi.org/10.1007/s11704-016-6061-x
RESEARCH ARTICLE |
M-generalization for multipurpose transactional data publication
Xianxian LI1,2, Peipei SUI1, Yan BAI3, Li-E WANG2()
1. School of Computer Science and Engineering, Beihang University, Beijing 100191, China
2. Guangxi Key Lab of Multi-source InformationMining & Security, Guangxi Normal University, Guilin 541004, China
3. Institute of Technology, University of Washington Tacoma, Tacoma WA 98402-3100, USA
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Abstract

Transactional data collection and sharing currently face the challenge of how to prevent information leakage and protect data from privacy breaches while maintaining high-quality data utilities. Data anonymization methods such as perturbation, generalization, and suppression have been proposed for privacy protection. However, many of these methods incur excessive information loss and cannot satisfy multipurpose utility requirements. In this paper, we propose a multidimensional generalization method to provide multipurpose optimization when anonymizing transactional data in order to offer better data utility for different applications. Our methodology uses bipartite graphs with generalizing attribute, grouping item and perturbing outlier. Experiments on real-life datasets are performed and show that our solution considerably improves data utility compared to existing algorithms.

Keywords anonymization      generalization      privacy protection      bipartite graph     
Corresponding Authors: Li-E WANG   
Just Accepted Date: 18 July 2016   Online First Date: 27 November 2017    Issue Date: 04 December 2018
 Cite this article:   
Xianxian LI,Peipei SUI,Yan BAI, et al. M-generalization for multipurpose transactional data publication[J]. Front. Comput. Sci., 2018, 12(6): 1241-1254.
 URL:  
http://journal.hep.com.cn/fcs/EN/10.1007/s11704-016-6061-x
http://journal.hep.com.cn/fcs/EN/Y2018/V12/I6/1241
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Xianxian LI
Peipei SUI
Yan BAI
Li-E WANG
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