Risk of Re-Identification Based on Euclidean Distance in Anonymized Data PWSCUP2015

  • Satoshi ItoEmail author
  • Hiroaki Kikuchi
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 7)


We propose a new method to re-identify anonymized data by using Euclidean distance between the original record and the anonymized record and evaluate the accuracy of the proposed method. In order to clarify performance of several anonymization methods used in the competition of the PWSCUP2015, we examine each of single methods and attempt to estimate the accuracy of the combination of some methods.



We thank NSC for synthesized micro data and thank all participants of PWSCUP2015 for the anonymized data to study.


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Graduate SchoolMeiji UniversityTokyoJapan

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