Predicting User Privacy Preferences based on Dynamic Interpersonal Relationships and Content Sensitivity Analysis

• Applicant: Professorin Dr.-Ing. Delphine Reinhardt
• Subject Area: Security and Dependability, Operating-, Communication- and Distributed Systems
• Term: from 2017 to 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 317687129

An increasing number of users are registered in online social media and contribute a tremendous amount of personal user-generated content. Sharing content can however endanger users' privacy and have serious consequences if such media are accessible to an inappropriate audience. Examples include job dismissals or loss of health-insurance benefits. To protect their privacy, users can manually control the release and access of their content using existing solutions. However, the current state-of-art has repeatedly been demonstrated to be inefficient in appropriately supporting users in this task. Both the commonly observed substantial number of users' contacts and the high sharing frequency make the manual selection of access control rules a cumbersome and time-consuming process. Due to the lack of intuitiveness of existing solutions, users often do not modify default sharing settings and renounce in updating them over time. This regularly results in sharing more content than desired with an unsuited audience, hence putting the users' privacy at stake. In contrast, other users refrain from sharing content online by fear of selecting an inappropriate audience due to the complexity of existing solutions. It hence potentially reduces the benefits drawn by the users from their online social network. The goal of this proposal is therefore to allow users to simultaneously take advantage of online social networks while better protecting their privacy. We aim at addressing the shortcomings of currently deployed solutions and providing the foundations for a novel form of access control mechanisms. A promising approach that we will explore in this project is to dynamically suggest sharing settings tailored to the users. We will examine how accurate suggestions can be generated based on features of interpersonal relationships between users and their social contacts and the sensitivity of the content to be published with regards to the users' privacy. To cater for the usability of our approach, we will follow a user-centric approach and develop a solution that is easy to use and comprehend by non-expert users in order to foster its acceptance at large scale. We will hence investigate and develop innovative methods to autonomously infer both the nature and the strengths of the users' social relationships from their communication patterns and the degree of content sensitivity based on its characteristics, respectively. The developed methods will respect users' privacy by preferring local processing of readily available personal data on their devices. In our project, we will hence adopt the novel approach of mining users' data to help them in better protecting their privacy, by providing them more fine-granular access control mechanisms while simultaneously reducing the associated configuration burden.

• DFG Programme: Research Grants