Abstract: Quantitative assessment of social network users’ privacy leakage can help users’ understand personal privacy, improve public privacy protection and prevention awareness, and also provide a basis for the design of personalized privacy protection methods. Current privacy quantitative assessment methods are mainly used to evaluate the protective effect of privacy protection methods, and are not able to effectively assess the privacy leakage risk of social network users. A quantitative evaluation method of social network users’ privacy leakage is proposed. Firstly, users’ subjective attribute sensitivity is calculated by Pearson similarity based on privacy preference matrix, and is averaged to obtain the objective attribute sensitivity. Attribute openness is calculated by the information entropy of posterior distribution which is inferenced by user sensitive attribute inference method. Transition probability and user importance is used to estimate the visible range of user data to calculate data visibility. Then, privacy score is calculated by aggregating attribute sensitivity, attribute openness, and data visibility. Finally, a fine-grained privacy evaluation is conducted based on user's privacy score, which supports dynamic evaluation of user privacy and provides a basis for personalized privacy protection. The experimental results on Sina Weibo data show that the proposed method can effectively quantify the user's privacy leakage status.

Key words: social network, privacy quantification, attribute inference, privacy protection