A Study from the Data Anonymization Competition Pwscup 2015

  • Hiroaki KikuchiEmail author
  • Takayasu Yamaguchi
  • Koki Hamada
  • Yuji Yamaoka
  • Hidenobu Oguri
  • Jun Sakuma
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9963)


Data anonymization is required before a big-data business can run effectively without compromising the privacy of the personal information it uses. It is not trivial to choose the best algorithm to anonymize some given data securely for a given purpose. In accurately assessing the risk of data being compromised, there should be a balance between utility and security. Therefore, using common pseudo microdata, we proposed a competition for the best anonymization and re-identification algorithms. This paper reports the results of the competition and the analysis of the effectiveness of the anonymization techniques. The competition results show that there is a trade-off between utility and security, and 20.9 % of records were reidentified on average.


Utility Measure Data Anonymization Competition Result Common Dataset Record Index 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Akiyama, H., Yamaguchi, K., Ito, S., Hoshino, N., Goto, T.: Usage and development of educational pseudo micro-data -sampled from national survey of family income and expenditure in 2004. Techn. Report Nat. Stat. Cent. (NSTAC) 16, 1–43 (2012). (in Japanese)Google Scholar
  2. 2.
    El Emam, K., Arbuckle, L.: Anonymizing Health Data Case Studies and Methods to Get You Started. O’Reilly, CA, USA (2013)Google Scholar
  3. 3.
    Kikuchi, H., Yamaguchi, T., Hamada, K., Yamaoka, Y., Oguri, H., Sakuma, J., Ice, F.: Quantifying the Risk of Re-identification and Utility in Data Anonymization. In: Proceedings of the IEEE 30th International Conference on Advanced Information Networking and Applications (AINA), pp. 1035–1042 (2016)Google Scholar
  4. 4.
    Machanavajjhala, A., Kifer, D., Gehrke, J., Venkitasubramaniam, M.: \(L\)-diversity: Privacy beyond k-anonymity. ACM Trans. Knowl. Discov. Data 1, 1 (2007). Article 3CrossRefGoogle Scholar
  5. 5.
    Sweeny, L.: \(k\)-anonymity. Int. J. Uncertainty Fuzziness Knowl. Based Syst. 10, 571–588 (2002)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Hiroaki Kikuchi
    • 1
    Email author
  • Takayasu Yamaguchi
    • 2
  • Koki Hamada
    • 3
  • Yuji Yamaoka
    • 4
  • Hidenobu Oguri
    • 5
  • Jun Sakuma
    • 6
  1. 1.Meiji UniversityNakano KuJapan
  2. 2.NTT DOCOMO, Inc.YokusukaJapan
  3. 3.NTT Secure Platform LaboratoriesMusashinoJapan
  4. 4.Fujitsu Laboratories Ltd.KawasakiJapan
  5. 5.NIFTY CorporationShinjuku-kuJapan
  6. 6.University of TsukubaTsukubaJapan

Personalised recommendations