AI를 활용한 대화형 텍스트 데이터의 개인정보 재식별 위험성 측정방안에 관한 연구
A Study on Measuring the Risk of Re-identification of Personal Information in Conversational Text Data using AI
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초록
최근 인공지능 기술 발전으로 다양한 챗봇이 등장하여 호텔 예약, 뉴스 확인, 법률 상담 등 일상작업을 효율적으로 수행하고 있다. 특히 ChatGPT와 같은 생성형 챗봇은 교육, 연구, 예술 분야에서자체 콘텐츠를 생성하는 등 활용 가능성을 확장하고 있다. 이러한 AI챗봇의 학습에는 고객 서비스대화 기록 등 방대한 양의 '대화형 텍스트 데이터'가 필요하지만, 정제되지 않은 대화형 텍스트 데이터의 학습으로 인해 국내외에서 AI챗봇에 대한 개인정보 침해 사례가 발생하고 있다. 본 연구는AI챗봇 학습에 사용되는 '대화형 텍스트 데이터'를 기반으로 데이터 내 포함되어 있는 개인정보 항목에 대한 재식별 위험성을 계량적으로 측정할 수 있는 방법론을 제안하고 있다. 제안 방법론에 대한 타당성 검증을 위해 가상의 대화형 데이터를 생성하여 자체실증을 하였으며, 외부 전문가 220명을 대상으로 설문조사를 실시하여 제안하는 방법론의 유의미함을 확인할 수 있었다.
Abstract
With the recent advancements in artificial intelligence, various chatbots have emerged, efficiently performing everyday tasks such as hotel bookings, news updates, and legal consultations. Particularly, generative chatbots like ChatGPT are expanding their applicability by generating original content in fields such as education, research, and the arts. However, the training of these AI chatbots requires large volumes of conversational text data, such as customer service records, which has led to privacy infringement cases domestically and internationally due to the use of unrefined data. This study proposes a methodology to quantitatively assess the re-identification risk of personal information contained in conversational text data used for training AI chatbots. To validate the proposed methodology, we conducted a case study using synthetic conversational data and carried out a survey with 220 external experts, confirming the significance of the proposed approach.
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