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A New Approach for Anonymizing Relational and Transaction Data

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Proceedings of the 2nd International Conference on Healthcare Science and Engineering (ICHSE 2018)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 536))

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Abstract

For scientific research and business decision purposes, the relational and transaction data often needs to be published. Because individuals’ privacy exists in this data, it needs to be appropriately anonymized before to be released. However, the privacy model (k, km)-anonymity cannot preserve the individual’s privacy with more than m items in transaction attribute. Also, it does not make the diversity constraint and cannot prevent attribute disclosure. Moreover, it considers all items be sensitive in the transaction attribute, which will cause serious information loss. In this paper, the (k, ρ)-anonymity model is proposed, which can solve the problem caused by (k, km)-anonymity. Then an anonymous approach is designed to achieve (k, ρ)-anonymity. Experiment results show that our approach is better than existing anonymous approach for publishing relational and transaction data in utility and security.

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Acknowledgements

The authors thank Dr. Giorgos Poulis for his kindly providing the source codes to achieve (k, km)-anonymity and the anonymous reviewers for their very valuable suggestions. This paper was supported by the National Natural Science Foundation of China (Nos. 61502111, 61763003, 61672176, 61762015, 61562007, 61662008), Guangxi Natural Science Foundation (No. 2016GXNSFAA380192), Guangxi 1000-Plan of Training Middle-aged/Young Teachers in Higher Education Institutions, Guangxi “Bagui Scholar” Teams for Innovation and Research Project, Guangxi Special Project of Science and Technology Base and Talents (No. AD16380008), and Guangxi Collaborative Innovation Center of Multisource Information Integration and Intelligent Processing.

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Authors and Affiliations

  1. Guangxi Key Lab of Multi-Source Information Mining and Security, Guangxi Normal University, Guilin, 541004, China

    Jinyan Wang & Jingli Wu

  2. School of Computer Science and Information Technology, Guangxi Normal University, Guilin, 541004, China

    Jinyan Wang, Siming Zhou, Jingli Wu & Chen Liu

Authors
  1. Jinyan Wang
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  2. Siming Zhou
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  3. Jingli Wu
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  4. Chen Liu
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Corresponding author

Correspondence to Jingli Wu .

Editor information

Editors and Affiliations

  1. Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, USA

    Chase Q. Wu

  2. Department of Mechanical Engineering , Texas Tech University, Lubbock, TX, USA

    Ming-Chien Chyu

  3. Department of Communications, Universitat Politècnica de València, Valencia, Spain

    Jaime Lloret

  4. School of Computer Science, Guangxi Normal University, Guilin, Guangxi, China

    Xianxian Li

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© 2019 Springer Nature Singapore Pte Ltd.

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Wang, J., Zhou, S., Wu, J., Liu, C. (2019). A New Approach for Anonymizing Relational and Transaction Data. In: Wu, C., Chyu, MC., Lloret, J., Li, X. (eds) Proceedings of the 2nd International Conference on Healthcare Science and Engineering . ICHSE 2018. Lecture Notes in Electrical Engineering, vol 536. Springer, Singapore. https://doi.org/10.1007/978-981-13-6837-0_18

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