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Optimization algorithm for k-anonymization of datasets with low information loss

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Abstract

Anonymization is the modification of data to mask the correspondence between a person and sensitive information in the data. Several anonymization models such as k-anonymity have been intensively studied. Recently, a new model with less information loss than existing models was proposed; this is a type of non-homogeneous generalization. In this paper, we present an alternative anonymization algorithm that further reduces the information loss using optimization techniques. We also prove that a modified dataset is checked whether it satisfies the k-anonymity by a polynomial-time algorithm. Computational experiments were conducted and demonstrated the efficiency of our algorithm even on large datasets.

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Acknowledgements

Part of this research is supported by the Funding Program for World-Leading Innovative R&D on Science and Technology, Japan. We thank Professor Wong for providing us with the programs used in our experiments.

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Authors and Affiliations

  1. Kansai University, 3-3-35 Yamate, Suita, Osaka, 564-8680, Japan

    Keisuke Murakami

  2. National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo, 101-8430, Japan

    Takeaki Uno

Authors
  1. Keisuke Murakami
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  2. Takeaki Uno
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Correspondence to Keisuke Murakami.

Appendix

Appendix

The gaps between \(\hbox {GCP}_\mathrm{t}\) and \(\hbox {GCP}_\mathrm{unsort}\) are computed by

$$\begin{aligned} \mathrm{Gap\_sort} = \frac{\hbox {GCP}_\mathrm{unsort} - \hbox {GCP}_\mathrm{t}}{ \hbox {GCP}_\mathrm{t}} , \end{aligned}$$
(12)

where \(\hbox {GCP}_\mathrm{unsort}\) represents the information loss of the anonymized dataset obtained using our algorithm without preliminarily sorting. Note that we do not compute Gap_sort for the instances of \(|\mathcal{T}|=100\hbox {k}\) because the datasets are not partitioned; thus, \(\hbox {GCP}_\mathrm{unsort}\) equals to \(\hbox {GCP}_\mathrm{t}\) (Tables 19, 20).

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Murakami, K., Uno, T. Optimization algorithm for k-anonymization of datasets with low information loss. Int. J. Inf. Secur. 17, 631–644 (2018). https://doi.org/10.1007/s10207-017-0392-y

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