Study on Record Linkage of Anonymizied Data

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 reported the result of the competition and the analysis on the effective of anonymization technique. The competition result reveals that there is a tradeoff between utility and security, and 20.9% records were re-identified in average

プロファイルと履歴を組織間で統合利用するプライバシ保護推薦システム

電気通信大学201