An Efficient Social Attribute Inference Scheme Based on Social Links and Attribute Relevance

Abstract

Social network is a critical component in mobile multimedia systems, where users share their videos, photos, and other media. However, the information (e.g., posts, user profiles, etc.) shared on the social network platforms usually reflects many users' personal (private) information, which could be mined and abused for malicious purposes. To address privacy concerns, many social network service providers adopted privacy-preserving mechanisms, e.g., anonymizing user identity, hiding users' profiles, etc. As a result, the attributes in user profiles are usually set up to be accessed only by friends to prevent privacy leakage. Several attacks have been proposed to infer the hidden attributes to Several the efficiency of current privacy-protecting mechanisms. Most of these solutions are based on the social links among users or their behaviors. In this paper, we systematically analyze the social features related to user privacy inference and found that there are relevances among social attributes, which has a great impact on inferring users' hidden attributes. According to our findings, we propose an efficient social attribute inference scheme based on social links and attribute relevance properties. We develop a relevance attribute inference method (ReAI) using random walks with restart. We analyze attribute relevance on inference performance and use Kulczynski measure to quantify attribute relevance as edge weights of attribute nodes in an improved social-Attribute network. We evaluate our method and compare it with the traditional attribute inference method. The results show that our method performs better than the traditional method. We also use Kulczynski measure and Information Gain Ratio to evaluate the improvements. The results show that the bigger relevance between attributes contributes to higher improvements.