공개 데이터집합에 대한 무조정, 실시간 프라이버시 보존
Unadjusted and Realtime Privacy Preservation for Publishing Data Set
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초록
본 연구는 사용자로 하여금 내용 조정 없는 원본 데이터집합을 실시간 접근 가능하게 하면서 프라이버시를 보존하는 방안을 제안한다. 프라이버시 보존을 위한 지금까지의 방법들은 개체노출 방지를 위한 k-익명화와 민감속성노출 방지를 위한 l-다양화를 적용함에 따른 데이터 사용성 저하가 불가피하였다. 본 연구에서는 개체노출을 허용하는 대신 l-다양화를 통한 민감속성노출 방지만으로 프라이버시를 보존하는 방안을 제안한다. 사용자에게 제공하는 민감속성값 집합은 민감속성 온톨로지로부터 의미적으로 근접한 내용들을 선택하여 단일 혹은 군집개체에 연결할 내용으로 구성함으로써 민감속성값 집합의 정보손실을 감소시겼다. 실험을 통하여 동일 다양성 기준으로 60% 내외의 민감속성값 집합 정보손실 감소를 확인하였다. 사용자가 접근의도에 부합하는 내용 획득 정도를 나타내는 정밀도를 기준으로 k-익명화 수준별로 데이터 사용성을 파악함으로써 개체노출 허용에 따른 데이터 사용성 저하 방지의 유용함을 고찰하였다.
Abstract
This study proposes an approach to enable users to access the unadjusted original dataset in real time while preserving privacy. While previous methods for privacy preservation have employed kanonymity for entity disclosure prevention and ldiversity for attribute disclosure prevention, this study suggests an approach that preserves privacy solely through attribute disclosure prevention via ldiversity, excluding entity disclosure prevention. By not implementing kanonymity, the adjustment of quasiidentifiers in published datasets became unnecessary, thereby preventing a decrease in data usability. By accessing an ontology of sensitive attributes and selecting semantically related values, a set of sensitive attribute values connected to a single entity or an entity cluster was constructed, thus reducing information loss within the provided set of sensitive attribute values for users.
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