Monitoring and recommending privacy settings in social networks

ABSTRACT Ensuring privacy of users of social networks is probably an unsolvable conundrum. It seems, however, that informed use of the existing privacy options by the social network participants may alleviate -or even prevent -some of the more drastic privacyaverse incidents. Unfortunately, recent surveys show that an average user is either not aware of these options or does not use them, probably due to their perceived complexity. It is therefore reasonable to believe that tools assisting users with two tasks: 1) understanding their social network behavior in terms of their privacy settings and broad privacy categories, and 2) recommending reasonable privacy options, will be a valuable tool for everyday privacy practice in a social network context. This paper presents early research that shows how simple machine learning techniques may provide useful assistance in these two tasks to Facebook users

Should I agree?:Delegating consent decisions beyond the individual

Obtaining meaningful user consent is increasingly problematic in a world of numerous, heterogeneous digital services. Current approaches (e.g. agreeing to Terms and Conditions) are rooted in the idea of individual control despite growing evidence that users do not (or cannot) exercise such control in informed ways. We consider an alternative approach whereby users can opt to delegate consent decisions to an ecosystem of third-parties including friends, experts, groups and AI entities. We present the results of a study that used a technology probe at a large festival to explore initial public responses to this reframing -- focusing on when and to whom users would delegate such decisions. The results reveal substantial public interest in delegating consent and identify differing preferences depending on the privacy context, highlighting the need for alternative decision mechanisms beyond the current focus on individual choice

Semantics-Enhanced Privacy Recommendation for Social Networking Sites

Privacy protection is a vital issue for safe social interactions within social networking sites (SNS). Although SNSs such as MySpace and Facebook allow users to configure their privacy settings, the task is difficult for normal users with hundreds of online friends. In this paper, I propose an intelligent semantics-based privacy configuration system, named SPAC, to automatically recommend privacy settings for SNS users. SPAC learns users’ privacy configuration patterns and make predictions by utilizing machine learning techniques on users’ profiles and privacy setting history. To increase the accuracy of the predicted privacy settings, especially in the context of heterogeneous user profiles, I enhance privacy configuration predictor by integrating it with structured semantic knowledge. This allows SPAC to make inferences based on additional source of knowledge, resulting in improved accuracy of privacy recommendation. Our experimental results have proven the effectiveness of our approach