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An Improved Algorithm of Individuation K-Anonymity for Multiple Sensitive Attributes

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Abstract

At present, most of privacy preserving approaches in data publishing are applied to single sensitive attribute. However, applying single-sensitive-attribute privacy preserving techniques directly into data with multiple sensitive attributes often causes leakage of large amount of private information. This paper focuses on the privacy preserving methods in data publishing for multiple sensitive attributes. It combines data anonymous methods based on lossy join with the idea of clustering. And it proposes an improved algorithm of individuation K-anonymity for multiple sensitive attributes—\( MSA(\alpha ,l) \) algorithm. By setting parameters \( \alpha \) and \( l \), it can restrain sensitive attribute values in equivalence class, to make a more balanced distribution of sensitive attributes and satisfy the demand of diversity, then this algorithm is applied to K-anonymity model. Finally, the result of experiment shows that this improved model can preserve the privacy of sensitive data, and it can also reduce the information hidden rate.

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Acknowledgements

This paper was supported by the National Natural Science Foundation of China (61402241, 61572260, 61373017, 61572261, 61472192); Scientific and Technological Support Project of Jiangsu Province (BE2015702).

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Authors and Affiliations

  1. College of Computer, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China

    Lin Zhang, Jie Xuan, Ruoqian Si & Ruchuan Wang

  2. Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing, 210003, Jiangsu Province, China

    Lin Zhang & Ruchuan Wang

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  1. Lin Zhang
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  2. Jie Xuan
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  3. Ruoqian Si
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  4. Ruchuan Wang
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Correspondence to Lin Zhang.

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Zhang, L., Xuan, J., Si, R. et al. An Improved Algorithm of Individuation K-Anonymity for Multiple Sensitive Attributes. Wireless Pers Commun 95, 2003–2020 (2017). https://doi.org/10.1007/s11277-016-3922-4

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  • DOI: https://doi.org/10.1007/s11277-016-3922-4

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