## 3 Materials and Methods  
In this study, based on Tables 1-4 and advice from industry professionals on information protection and security, we analyzed 39 items related to personal information to detect personal information that may arise in SNS and AI chatbots. We defined 33 items and constructed a personal information tag set. We developed guidelines for creating data on defined personal information items and collected data accordingly. We then transformed the collected data into JSON format suitable for AI learning based on the data structure definition document and conducted model training. Consequently, we developed a named entity recognizer to detect personal information.  
First, we established a privacy-specific named entity tag set. Traditional NER tag sets that contain multiple items within a single tag fail to detect certain personal information items and inaccurately detect information. Consequently, we identified 39 personal information items as appropriate for inclusion in a privacy-specific named entity tag set. We evaluated the detectability of these items using NER and assessed their exposure risk when used alone or in combination to consider the extent of de-identification required. A personal information tag set comprising 33 items, frequently used in interactive texts and posing significant exposure risks, was constructed. This set includes names, nicknames, gender, height, weight, account numbers, card numbers, and sensitive unique identification numbers, such as resident registration, passport, driver's license, and alien registration.  
Second, we collected interactive text data containing personal information. We established guidelines for generating virtual dialog data incorporating personal information and refined them through an inspection process to ensure their appropriateness for natural dialogs.  
Third, we created a training dataset using a data structure definition document as a guide. This involved labeling dialog topics, general named entities, PNEs, and speakers, with PNEs labeled according to the specified tag set.  
Fourth, using the begin-inside-outside (BIO) tagging method commonly used in NER, we preprocessed data for deep learning training, where 'B', 'I', and 'O' denote the beginning of a named entity, interior, and external parts not included in the named entity, respectively.  
Fifth, the related literature helped us select the deep learning model. We selected the transformer-based BERT and ELECTRA models because they demonstrated high performance in natural language processing.  
Sixth, we conducted experiments using the KPF-BERT and KR-ELECTRA models for BERT and ELECTRA. We used PyTorch 1.13 as a deep learning framework and implemented the model on a server equipped with four NVIDIA RTX A5000 GPUs (Nvidia, Santa Clara, CA, USA) to train and evaluate the models. We then identified directions for improvement.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Grade** & **Type** & **Personal Information Items** \\ \hline \multirow{4}{*}{Grade 3} & Telecommunications information & IP information, MAC address, site visit history, phone call history, log files, and cookies. \\ \cline{2-3}  & Processed information & Statistical information and subscriber tendency. \\ \cline{2-3}  & Limited personal identification & Membership information, employee number, and personally identifiable information for internal use. \\ \hline \hline \end{tabular}
\end{table}
Table 4: The types of personal information according to the Personal Information Protection Commission’s guidelines (Grade 3) [26].