Preventing A False Light Caused by k-anonymity with Dividing Databases

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  • データベース分割再構成法によるk-匿名化が誘発する濡れ衣の軽減

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個人の情報を保護したデータ開示法の1つにk-匿名化がある．k-匿名化されたデータを人間が閲覧した際に，データに含まれた人間に対して不利益を生ずるような推測がなされる場合がある．本研究ではこの現象をk-匿名化が誘発する濡れ衣と呼び，濡れ衣を発生させうる属性を持つ機微なレコードに着目し，濡れ衣の発生を軽減させるk-匿名化法を提案する．実データに対して濡れ衣を発生させうる機微属性を付与したデータセットを用いて実験を行い，提案手法を用いると濡れ衣を軽減させたk-匿名化を実現できることを確認した．

In the field of privacy preserving data mining, k-anonymity is a representative model for protecting privacy. However, when people see k-anonymized data, a person who provides his/her data is misleadingly suspected as a bad guy due to the information which actually has nothing to do with him/her. We define such problem as a false light caused by k-anonymization, and define a record which has an attribute causing a false light as a sensitive record. We propose k-anonimization algorithms which pay attention to sensitive records in order to prevent a false light. In the experiments, we confirmed that proposed method can decrease a probability of occurrence of a false light.

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