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A Study from the Data Anonymization Competition Pwscup 2015

  • Conference paper
  • First Online:
Data Privacy Management and Security Assurance (DPM 2016, QASA 2016)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 9963))

Abstract

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.

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Notes

  1. 1.

    “Ice” and “fire” refer to anonymization and re-identification attempts, respectively.

  2. 2.

    http://www.nstac.go.jp/services/ippan-microdata.html (in Japanese) and http://www.nstac.go.jp/en/services/public.html.

References

  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)

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  2. El Emam, K., Arbuckle, L.: Anonymizing Health Data Case Studies and Methods to Get You Started. O’Reilly, CA, USA (2013)

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  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)

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  4. Machanavajjhala, A., Kifer, D., Gehrke, J., Venkitasubramaniam, M.: \(L\)-diversity: Privacy beyond k-anonymity. ACM Trans. Knowl. Discov. Data 1, 1 (2007). Article 3

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  5. Sweeny, L.: \(k\)-anonymity. Int. J. Uncertainty Fuzziness Knowl. Based Syst. 10, 571–588 (2002)

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Author information

Authors and Affiliations

  1. Meiji University, 4-21-1 Nakano, Nakano Ku, Tokyo, 164-8525, Japan

    Hiroaki Kikuchi

  2. NTT DOCOMO, Inc., Yokusuka, Kanagawa, Japan

    Takayasu Yamaguchi

  3. NTT Secure Platform Laboratories, Musashino, Tokyo, Japan

    Koki Hamada

  4. Fujitsu Laboratories Ltd., Kawasaki, Kanagawa, Japan

    Yuji Yamaoka

  5. NIFTY Corporation, Shinjuku-ku, Tokyo, Japan

    Hidenobu Oguri

  6. University of Tsukuba, Tsukuba, Ibaraki, Japan

    Jun Sakuma

Authors
  1. Hiroaki Kikuchi
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  2. Takayasu Yamaguchi
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  3. Koki Hamada
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  4. Yuji Yamaoka
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  5. Hidenobu Oguri
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  6. Jun Sakuma
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Corresponding author

Correspondence to Hiroaki Kikuchi .

Editor information

Editors and Affiliations

  1. Dipartimento di Informatica, Università degli Studi di Milano, Crema, Italy

    Giovanni Livraga

  2. University of Skövde , Skövde, Sweden

    Vicenç Torra

  3. University of Urbino , Urbino, Italy

    Alessandro Aldini

  4. IIT National Research Council CNR , Pisa, Italy

    Fabio Martinelli

  5. Technische Universität Darmstadt , Darmstadt, Germany

    Neeraj Suri

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© 2016 Springer International Publishing AG

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Kikuchi, H., Yamaguchi, T., Hamada, K., Yamaoka, Y., Oguri, H., Sakuma, J. (2016). A Study from the Data Anonymization Competition Pwscup 2015 . In: Livraga, G., Torra, V., Aldini, A., Martinelli, F., Suri, N. (eds) Data Privacy Management and Security Assurance. DPM QASA 2016 2016. Lecture Notes in Computer Science(), vol 9963. Springer, Cham. https://doi.org/10.1007/978-3-319-47072-6_16

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  • DOI: https://doi.org/10.1007/978-3-319-47072-6_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47071-9

  • Online ISBN: 978-3-319-47072-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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