How to Handle Excessively Anonymized Datasets

• Nojima Ryo

  National Institute of Information and Communications Technology, NICT

• Oguri Hidenobu

  FUJITSU LABORATORIES LTD.

• Kikuchi Hiroaki

  Meiji University

• Nakagawa Hiroshi

  The University of Tokyo

• Hamada Koki

  NTT Secure Platform Laboratories

• Murakami Takao

  National Institute of Advanced Industrial Science and Technology, AIST

• Yamaoka Yuji

  FUJITSU LABORATORIES LTD.

• Watanabe Chiemi

  University of Tsukuba

Abstract

Many companies and organizations have been collecting personal data with the aim of sharing it with partners. To prevent re-identification, the data should be anonymized before being shared. Although many anonymization methods have been proposed thus far, choosing one from them is not trivial since there is no widely accepted criteria. To overcome this situation, we have been conducting a data anonymization and re-identification competition, called PWS CUP, in Japan. In this paper, we introduce a problem appeared at the competition, named an excessive anonymization, and show how to formally handle it.

Journal

• Journal of Information Processing

  Journal of Information Processing 26 (0), 477-485, 2018

  Information Processing Society of Japan

Keywords

• PWS CUP
• excessively anonymized datasets
• distance function
• bounds

Details

• CRID: 1390282763014799616
• NII Article ID: 130007397267
• DOI: 10.2197/ipsjjip.26.477
• ISSN: 18826652
• Web Site: https://www.jstage.jst.go.jp/article/ipsjjip/26/0/26_477/_pdf
• Text Lang: en
• Data Source:
  • JaLC
  • Crossref
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed