프라이버시 보존 의료 마이크로데이터 수집 및 분석
Privacy-Preserving Collection and Analysis of Medical Microdata
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4차 산업혁명의 도래와 함께 인공지능, 빅데이터, 사물인터넷, 클라우드 컴퓨팅 등의 첨단 정보기술이 다양한 산업 분야에서 혁신을 이끌고 있다. 이 기술들은 방대한 양의 데이터를 생성하고있으며, 많은 기업들이 이를 활용하고 있다. 그러나 개인 데이터 수집 시 발생할 수 있는 프라이버시 침해 위험으로 인해 사용자들은 민감한 정보 제공을 망설이고 있다. 특히 의료 분야에서는환자의 병명과 같은 민감한 정보 수집이 큰 도전이 되고 있으며, 프라이버시 문제가 데이터 수집과 분석의 장애가 되고 있다. 본 연구는 프라이버시 보호를 유지하면서도 통계적 정보를 효과적으로 추출할 수 있는 의료 데이터 수집 및 분석 기법을 제안한다. 제안 기법은 기존의 단순한 데이터 수집을 넘어서, 프라이버시를 보장하면서 수집된 데이터에서 통계적 정보를 효과적으로 추출하는 방법을 포함한다. 실제 데이터를 이용한 성능 평가에서는 제안된 기법이 기존 방법보다더 효과적으로 프라이버시를 보존하며 통계적 정보를 도출할 수 있음을 입증한다.
Abstract
With the advent of the Fourth Industrial Revolution, cutting-edge technologies such as artificial intelligence, big data, the Internet of Things, and cloud computing are driving innovation across industries. These technologies are generating massive amounts of data that many companies are leveraging. However, there is a notable reluctance among users to share sensitive information due to the privacy risks associated with collecting personal data. This is particularly evident in the healthcare sector, where the collection of sensitive information such as patients' medical conditions poses significant challenges, with privacy concerns hindering data collection and analysis. This research presents a novel technique for collecting and analyzing medical data that not only preserves privacy, but also effectively extracts statistical information. This method goes beyond basic data collection by incorporating a strategy to efficiently mine statistical data while maintaining privacy. Performance evaluations using real-world data have shown that the propose technique outperforms existing methods in extracting meaningful statistical insights.
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