Ice and Fire: Quantifying the Risk of Re-identification and Utility in Data Anonymization

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 com...

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Bibliographic Details
Main Authors: Kikuchi, Hiroaki, Yamaguchi, Takayasu, Hamada, Koki, Yamaoka, Yuji, Oguri, Hidenobu, Sakuma, Jun
Format: Conference Proceeding
Language:English
Subjects:
Algorithms
anonymity
Business
Competition
Conferences
Data privacy
Fires
Ice
Indexes
k-anonymity
Laboratories
Measurement
Platforms
quantification
re-identified risk
Risk
Security
Utilities
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Summary: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.
ISSN:1550-445X
2332-5658
DOI:10.1109/AINA.2016.151