Privacy policy generation for images in online social networks

Abstract

Image sharing is a service offered by many online social networks. In order to preserve privacy of images, users need to think through and set the privacy settings for each image that they upload. This is difficult for two main reasons: First, research shows that many times users do not know their own privacy preferences, but only become aware of them over time. Second, even when users know their privacy prefer ences, specifying these policies is cumbersome and requires too much effort, interfering with the quick sharing behavior expected on an social network. Accordingly, this thesis proposes an agent-based approach, PELTE, that predicts the privacy setting of images using their content tags. Each user agent makes use of the privacy settings that its user have set for previous images to predict the privacy setting for a new uploaded one automatically. When in doubt, the agent analyzes the sharing behavior of other trusted agents to make a recommendation to its user about what is private. Contrary to existing approaches that assume a centralized online social network where privacy is set by accessing all the available images, PELTE is distributed and thus each agent can only view the privacy settings of the images that it has shared or those that have been shared with it. Our simulations on a real-life dataset show that PELTE can ac curately predict privacy settings even when a user is new in a online social network, she has shared a few images with others, the images have only a few tags or the user’s friends have varying privacy preferences.