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.
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Acknowledgements
This research was supported by the National Natural Science Foundation of China (Grant Nos. 61662008, 61272535, 61502111), Guangxi “Bagui Scholar” Teams for Innovation and Research Project, Guangxi Collaborative Innovation Center of Multi-source Information Integration and Intelligent Processing, Guangxi Natural Science Foundation (2015GXNSFBA139246, 2014GXNSFBA118288 and 2013GXNSFBA019263), and Guangxi Special Project of Science and Technology Base and Talents (AD16380008).
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Xianxian Li is a professor of Guangxi Normal University, China and a PhD supervisor of Beihang University, China. His current research interests mainly include data security and software theory.
Peipei Sui received her bachelor and master degrees from Yanshan University, China. She is currently pursuing the PhD degree with the School of Computer Science and Engineering, Beihang University, China. Her current research interests include social network data, spatial-temporal data mining and data privacy.
Yan Bai is an associate professor of University of Washington Tacoma, USA. Her general research interests are in the areas of cyber security and computer networking.
Li-E Wang received her bachelor and master degrees from Hunan University, China. Now she is an associate professor of Guangxi Normal University, China. Her current research interests mainly include data security and computer networking.
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Li, X., Sui, P., Bai, Y. et al. M-generalization for multipurpose transactional data publication. Front. Comput. Sci. 12, 1241–1254 (2018). https://doi.org/10.1007/s11704-016-6061-x
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DOI: https://doi.org/10.1007/s11704-016-6061-x