Towards the Understanding of Private Content – Content-based Privacy Assessment and Protection in Social Networks

Abstract

In the wake of the Facebook data breach scandal, users begin to realize how vulnerable their per-sonal data is and how blindly they trust the online social networks (OSNs) by giving them an inordinate amount of private data that touch on unlimited areas of their lives. In particular, stud-ies show that users sometimes reveal too much information or unintentionally release regretful messages, especially when they are careless, emotional, or unaware of privacy risks. Additionally, friends on social media platforms are also found to be adversarial and may leak one’s private in-formation. Threats from within users’ friend networks – insider threats by human or bots – may be more concerning because they are much less likely to be mitigated through existing solutions, e.g., the use of privacy settings. Therefore, we argue that the key component of privacy protection in social networks is protecting sensitive/private content, i.e. privacy as having the ability to control dissemination of information. A mechanism to automatically identify potentially sensitive/private posts and alert users before they are posted is urgently needed. In this dissertation, we propose a context-aware, text-based quantitative model for private in-formation assessment, namely PrivScore, which is expected to serve as the foundation of a privacy leakage alerting mechanism. We first explicitly research and study topics that might contain private content. Based on this knowledge, we solicit diverse opinions on the sensitiveness of private infor-mation from crowdsourcing workers, and examine the responses to discover a perceptual model behind the consensuses and disagreements. We then develop a computational scheme using deep neural networks to compute a context-free PrivScore (i.e., the “consensus” privacy score among average users). Finally, we integrate tweet histories, topic preferences and social contexts to gener-ate a personalized context-aware PrivScore. This privacy scoring mechanism could be employed to identify potentially-private messages and alert users to think again before posting them to OSNs. It could also benefit non-human users such as social media chatbots.