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How to Handle Excessively Anonymized Datasets

Year: 2018
Type: article
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.
DOI: 10.2197/ipsjjip.26.477
Authors: Ryo Nojima, Hidenobu Oguri, Hiroaki Kikuchi, Hiroshi Nakagawa, Koki Hamada, Takao Murakami, Yuji Yamaoka, Chiemi Watanabe
Institutions: National Institute of Information and Communications Technology, Fujitsu (Japan), Meiji University, The University of Tokyo, NTT (Japan), National Institute of Advanced Industrial Science and Technology, University of Tsukuba
Language: English
Open Access status: diamond
OpenAlex ID: W2808564455
FWCI: 0.242
Cites: 5
Cited by: 3
Related to: 10