관계형 데이터베이스에서 데이터 그룹화를 이용한 익명화처리 기법
The De-identification Technique Using Data Grouping in Relational Database
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정부 3.0 공공정보 공유 및 개방, 소셜네트워크서비스의 활성화 그리고 사용자 간의 공유 데이터 증가로 인터넷상에노출되는 사용자의 개인 정보가 증가하고 있다. 이에 따라 프라이버시를 지키기 위한 익명화 알고리즘이 등장하였으며관계형 데이터베이스에서의 익명화 알고리즘은 k-익명성(k-anonymity)을 시작으로 ℓ-다양성(ℓ-diversity), t-밀집성(t-closeness)으로 발전하였다. 익명화 알고리즘의 성능 향상 부분은 계속해서 효율적인 방법이 제안되고 있지만, 기업이나 공공기관에서는 알고리즘 성능의 향상보다는 전체적인 익명화 처리 방법이 필요한 실정이다. 본 논문에서는관계형 데이터베이스에서 데이터의 그룹화를 이용하여 k-익명성, ℓ-다양성, t-밀집성 알고리즘을 처리하는 과정을 구체화하였다.
Abstract
Personal information exposed in the Internet is increasing by the public data opening and sharing, vitalization of SNS(Social Network Service) and growth of information shared between users. Exposed personal information in the Internet can infringe upon targeted users using linkage attack or background attack. To prevent these attack De-identification models were appeared a few years ago. The ’k-anonymity’ has been introduced in the first place, and the ‘ℓ-diversity’ and ’t-closeness’ have been followed up as solutions, and diverse algorithms have been being suggested for performance improvement nowadays. However, industry or public sectors actually needs a whole solution as a system for the de-identification process rather than performance of the de-identification algorithm. This paper explains a way of de-identification techique for ‘k-anonymity’, ‘ℓ-diversity’, and ’t-closeness’ algorithm using QI(Quasi-Identifier) grouping method in the relational database.
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