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Permutation anonymization

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Abstract

In data publishing, anonymization techniques have been designed to provide privacy protection. Anatomy is an important techniques for privacy preserving in data publication and attracts considerable attention in the literature. However, anatomy is fragile under background knowledge attack and the presence attack. In addition, anatomy can only be applied into limited applications. To overcome these drawbacks, we propose an improved version of anatomy: permutation anonymization, a new anonymization technique that is more effective than anatomy in privacy protection, and in the meanwhile is able to retain significantly more information in the microdata. We present the detail of the technique and build the underlying theory of the technique. Extensive experiments on real data are conducted, showing that our technique allows highly effective data analysis, while offering strong privacy guarantees.

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China (NO.61202007), Chinese Postdoctoral Science Foundation Funded Project (No.2013M540323) and the Natural Science Foundation of NingBo (NO.2013A610110).

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Authors and Affiliations

  1. Information Center, National Natural Science Foundation of China, Beijing, China

    Dong Li

  2. School of Information Science and Engineering, Ningbo University, Ningbo, Zhejiang, China

    Xianmang He & Huahui Chen

  3. Advanced Analytics Institute, University of Technology Sydney, Ultimo, NSW, Australia

    LongBin Cao

  4. School of Computer Science and Technology, Fudan University, Shanghai, China

    Xianmang He

Authors
  1. Dong Li
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  2. Xianmang He
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  3. LongBin Cao
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  4. Huahui Chen
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Correspondence to Xianmang He.

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Li, D., He, X., Cao, L. et al. Permutation anonymization. J Intell Inf Syst 47, 427–445 (2016). https://doi.org/10.1007/s10844-015-0373-4

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  • DOI: https://doi.org/10.1007/s10844-015-0373-4

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