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Flexible sensitive K-anonymization on transactions

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Abstract

In recent years, privacy breaches have been a great concern on the published data. Only removing one’s personal identification information is not sufficient to protect individual’s privacy. Privacy preservation technology for published data is devoted to preventing re-identification and retaining the useful information in published data. In this work, we propose a novel algorithm to deal with sensitive and quasi-identifier items, respectively, in transactional data. The proposed algorithm maintains at least the same or a stronger privacy level for transactional data with 1/k. In numerical experiments, our proposed algorithm shows better running time and better data utility.

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Authors and Affiliations

  1. Library and Information Center, National University of Kaohsiung, Kaohsiung, 81148, Taiwan

    Yu-Chuan Tsai

  2. Department of Information Management, National University of Kaohsiung, Kaohsiung, 81148, Taiwan

    Shyue-Liang Wang & I-Hsien Ting

  3. Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, 81148, Taiwan

    Tzung-Pei Hong

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  1. Yu-Chuan Tsai
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  2. Shyue-Liang Wang
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  3. I-Hsien Ting
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  4. Tzung-Pei Hong
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Correspondence to Shyue-Liang Wang.

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Tsai, YC., Wang, SL., Ting, IH. et al. Flexible sensitive K-anonymization on transactions. World Wide Web 23, 2391–2406 (2020). https://doi.org/10.1007/s11280-020-00798-8

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