Multi-dimensional User-sensitive Information Portrait for Social Networks

• Published in: 2022 IEEE 21st International Conference on Ubiquitous Computing and Communications (IUCC/CIT/DSCI/SmartCNS) pp. 106 - 115
• Main Authors: Shan, Fangfang, Wang, Mengyi, Sun, Huifang, Li, Fuyang, Ji, Peiyu, Zhang, Menghan
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2022
• Subjects: Access control; Chinese social networks; Data privacy; Dictionaries; Privacy; privacy protection; sensitive information; sensitive information portrait; Sensitivity; Social networking (online); Visualization
• DOI: 10.1109/IUCC-CIT-DSCI-SmartCNS57392.2022.00029

In response to the current situation that social network users have little awareness of privacy protection, users will disclose their privacy information in the information they post when using social network platforms, in order to raise awareness of personal privacy protection among social network users and help them understand the importance of protecting their privacy information. Therefore, we propose a user multi-dimensional sensitive information portrait model based on social networks, use the TF-IDF algorithm based on bag-of-words model to calculate the sensitivity of sensitive information, classify sensitive information into high, medium and low sensitivity levels according to the importance of sensitive information to users, and carve a multi-dimensional sensitive information portrait of group users. By constructing two sensitive information dictionaries, using the improved FlashText algorithm combined with the regular expression string matching algorithm and the sure inverse order circular view matching algorithm to extract user sensitive information from the basic information of social network users and the historical data posted by users in social networks, and carving a multi-dimensional sensitive information portrait of users according to sensitive information and sensitivity, users can replace sensitive information according to their needs to achieve the purpose of user privacy protection. Through experimental evaluation, our scheme achieves an accuracy of 93.63% for the extraction of sensitive information.