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금융 데이터 상에서의 차분 프라이버시 모델 정립 연구

A Study on a Differentially Private Model for Financial Data

박철희|최대선|홍도원|김현일
한국정보보호학회 (Korea Institute Of Information Security And Cryptology)|2017.12|정보보호학회논문지 (Journal of The Korea Institute of Information Security and Cryptology)|제27권 제6호|pp. 1519-1534|DOI https://doi.org/10.13089/JKIISC.2017.27.6.1519

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키워드

De-identificationDifferential privacyFinancial data
주제분류컴퓨터학

초록

데이터 비식별화 기법은 데이터 내에 속한 개인 정보에 대한 프라이버시를 만족하면서 동시에 데이터 분석가들에게 유용한 정보를 습득할 수 있게 하는 반드시 필요한 기술 중 하나이다. 그러나  익명성과 같은 기존의 비식별화기법은 공격자의 사전지식(Background knowledge)에 근본적으로 취약한 약점을 지니고 있다. 하지만 차분 프라이버시(Differential privacy)는 기존의 비식별화 기법들과는 다르게 개인 정보에 대한 강력한 안전성을 보장하는모델로써 최근 들어 이에 대한 연구가 매우 활발히 진행 중에 있다. 본 논문은 이러한 차분 프라이버시가 적용된 기술에 대한 연구 및 분석을 통해 금융 데이터 상에서의 차분 프라이버시 모델을 정립하였으며 이러한 모델들은 금융데이터 상에서 유용하게 사용될 수 있음을 입증하였다.

Abstract

Data de-identification is the one of the technique that preserves individual data privacy and provides useful information of data to the analyst. However, original de-identification techniques like -anonymity have vulnerabilities to background knowledge attacks. On the contrary, differential privacy has a lot of researches and studies within several years because it has both strong privacy preserving and useful utility. In this paper, we analyze various models based on differential privacy and formalize a differentially private model on financial data. As a result, we can formalize a differentially private model on financial data and show that it has both security guarantees and good usefulness.

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