Journal of Information Processing
Online ISSN : 1882-6652
ISSN-L : 1882-6652
How to Handle Excessively Anonymized Datasets
Ryo NojimaHidenobu OguriHiroaki KikuchiHiroshi NakagawaKoki HamadaTakao MurakamiYuji YamaokaChiemi Watanabe
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2018 Volume 26 Pages 477-485


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.

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© 2018 by the Information Processing Society of Japan
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