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Characterizations of Local Recoding Method on k-Anonymity

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Advances in Network-Based Information Systems (NBiS 2018)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 22))

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Abstract

k-Anonymity is one of the most widely used techniques for protecting the privacy of the publishing datasets by making each individual not distinguished from at least k-1 other individuals. The local recoding method is an approach to achieve k-anonymization through suppression and generalization. The method generalizes the dataset at the cell level. Therefore, the local recoding could achieve the k-anonymization with only a small distortion. As the optimal k-anonymity has been proved as the NP-hard problem, the plenty of optimal algorithm local recoding has been proposed. In this research, we study the characteristics of the local recoding method. In addition, we discover the special characteristic dataset that all generalization hierarchies of each quasi-identifier are identical, called an “Identical Generalization Hierarchy” (IGH) data. We also compare the efficiency of the well-known algorithms of the local recoding method on both \(non-IGH\) and IGH data.

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Author information

Authors and Affiliations

  1. Computer Engineering Department, Chiang Mai University, Chiang Mai, Thailand

    Waranya Mahanan & Juggapong Natwichai

  2. Department of Industrial Engineering, University of Arkansas, Arkansas, USA

    W. Art Chaovalitwongse

Authors
  1. Waranya Mahanan
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  2. Juggapong Natwichai
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  3. W. Art Chaovalitwongse
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Corresponding author

Correspondence to Waranya Mahanan .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Faculty of Management, Comenius University in Bratislava, Bratislava, Slovakia

    Natalia Kryvinska

  3. Faculty of Business Administration, Rissho University, Tokyo, Japan

    Tomoya Enokido

  4. Department of Advanced Sciences, Hosei University, Tokyo, Japan

    Makoto Takizawa

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Mahanan, W., Natwichai, J., Art Chaovalitwongse, W. (2019). Characterizations of Local Recoding Method on k-Anonymity. In: Barolli, L., Kryvinska, N., Enokido, T., Takizawa, M. (eds) Advances in Network-Based Information Systems. NBiS 2018. Lecture Notes on Data Engineering and Communications Technologies, vol 22. Springer, Cham. https://doi.org/10.1007/978-3-319-98530-5_56

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