Ice and Fire: Quantifying the Risk of Re-identification and Utility in Data Anonymization
Hiroaki Kikuchi, Takayasu Yamaguchi, Koki Hamada, Yuji Yamaoka, Hidenobu Oguri, Jun Sakuma · 2016
Data anonymization is required before a big-data business can run effectively without compromising the privacy of 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 needs to be a balance between utility and security. Therefore, using common pseudo microdata, we propose a competition for the best anonymization and re-identification algorithm. The paper addresses the aim of the competition, the target microdata, sample algorithms, utility and security metrics. The design of an evaluation platform is also considered.