## 3 Materials and Methods
### Interactive Data Collection and Processing  
Considering the widespread use of SNS and AI chatbots, we collected dialog data. We conducted a preliminary independent investigation of various platforms, such as KakaoTalk, Twitter, Facebook, AI Hub, Naver News comments, YouTube comments, and online shopping mall comments. According to the investigation, personal information was rare in open spaces such as Naver News, YouTube, and online shopping mall comments. However, in the clothing category of online shopping malls, we observed some personal information related to body size. On Facebook, specific groups, such as job seekers or affiliated schools, often exposed information related to businesses for job opportunities, contact details of responsible individuals, emails, and associated information. As Twitter allows users to remain anonymous, the names and account numbers in transactional posts are often easily revealed. Although the AI Hub data contained some personal information, they were of limited type and quantity, masked, and therefore not useful. Leaking personal information from interactive data is a serious problem; however, the amount of personal information in everyday dialogs is minimal. After examining the KakaoTalk data of six individuals (including three senior-level researchers with over ten years of experience, two manager-level researchers with over four years of experience, and one assistant-level researcher with less than four years of experience), we identified less than 20 instances of dialogs revealing personal information, excluding names and nicknames, over six months to one year. Furthermore, we collected data through crowdsourcing because the interactive text contained sensitive personal information. However, the small sample size prevented the collection of the desired amount of data. We collected interactive personal information and processed it as follows. First, we constructed dialog data containing personal information based on various topics. Interactive data refer to dialogs on various platforms, such as messengers, social media, posts, comments, and call centers. We classified the dialog topics into eight types, as listed in Table 7, and defined the personal information items corresponding to each type. Dialog topics were categorized as \"personal and relationships\", \"housing and life\", \"shopping and trading\", \"public services\", \"leisure and entertainment\", \"work and occupation\", \"beauty and health\", and \"learning and career\". We categorized the prioritized dialog types into all categories except \"personal and relationships\" and classified dialogs that did not fall into any other category as \"personal and relationships\". We defined the personal information categories for each dialog type to minimize duplication and included many items within each dialog set. \begin{table}
\begin{tabular}{c c} \hline
**Dialog Topic** & **Personal Information Items** \\ \hline Personal and relationships & Name, nickname, date of birth, age, gender, religion, nationality, military unit, etc. (almost all personal information items can be included) \\ \hline Housing and life & Address, place, and license plate number \\ \hline Shopping and trading & Name, date of birth, mobile phone number, card number, account number, email address, ID, and URL \\ \hline Public services &
\begin{tabular}{c} Resident registration number, alien number, passport number, driver’s license number, general phone/FAX number, and IP information \\ \end{tabular} \\ \hline Leisure and entertainment & Nickname, club/society, and ID \\ \hline Work and occupation & Workplace, department, and job title/position \\ \hline Beauty and health & Age, gender, height, weight, and nhood type \\ \hline Learning and career & School, grade, and major \\ \hline \end{tabular}
\end{table} 
Table 7: Personal information items by dialog topic. Second, we constructed the dialog data using everyday dialog sentences, each consisting of a minimum of three turns and an average of four turns.