Think before you share in OSNs: Textual content and connection weight put you at higher privacy risk

Abstract

The widespread use of OSNs has brought forward the issue of privacy protection over OSNs, as sensitive information of users needs to remain private. Most users are unaware of possible privacy risks associated with sharing personal information in their accounts. Privacy settings of OSNs focus on protecting users' information just by providing them with means of configuring the audience of shared information. As such, privacy risk estimation (or scoring) is a hot topic in the field of OSN research and aims to develop risk measuring tools to ensure user privacy in OSNs. Conventional studies in the area often rely on synthetically generated or survey-based data and do not make any effort to infer private attribute values of users to utilize inference success in privacy scoring of these users. In this study, we propose a novel framework that involves populating a response matrix by using attribute inference and obtaining network aware-risk scores not just by using users' connections but weights of these connections as well. We perform attribute inference of users based on both their textual contents and connections. Our rule-based inference mechanism employed on contents produces inference accuracies ranging from 0.54 to 1.0 depending on the attribute at hand. On the other hand, the inference mechanism involving users' social connections produces inference accuracies of 1.0 almost for all of the considered attributes. We present results and challenges of attribute inference and use inferred attributes in privacy risk scoring. In addition, unlike existing works, we use and show that social tie strengths have to be taken into account in network-aware privacy risk scoring.

Keywords

• Facebook
• privacy risk estimation
• social tie strength
• online social networks.

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