Abstract
Transactional data about individuals is increasingly being collected to support many important real-life applications ranging from healthcare to marketing. Thus, privacy issues in sharing transactional data among different parties have attracted considerable research interest in recent years. Due to the high-dimensionality and sparsity of transactional data, existing privacy-preserving techniques will incur excessive information loss. We propose a hybrid optimization approach for anonymizing transactional data through integrating different anonymous techniques. Experimental results verify that our approach significantly outperforms the current state-of-the-art algorithms in terms of data utility.
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Acknowledgment
The research is supported by the National Key Basic Research Program of China (973 Program, No. 2012CB326403), National Science Foundation of China (No. 61272535), 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 (Nos. 2015GXNSFBA139246, 2013GXNSFBA019263, 2014GXNSF BA118288), Science and Technology Research Projects of Guangxi Higher Education (Nos. 2013YB029, 2015YB032), the Guangxi Science Research and Technology Development Project (No. 14124004-4-11) and Youth Scientific Research Foundation of Guangxi Normal University.
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Wang, Le., Li, X. (2015). A Hybrid Optimization Approach for Anonymizing Transactional Data. In: Wang, G., Zomaya, A., Martinez, G., Li, K. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2015. Lecture Notes in Computer Science(), vol 9532. Springer, Cham. https://doi.org/10.1007/978-3-319-27161-3_11
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DOI: https://doi.org/10.1007/978-3-319-27161-3_11
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