Biblio

The increasing complexity and ubiquity in user connectivity, computing environments, information content, and software, mobile, and web applications transfers the responsibility of privacy management to the individuals. Hence, making it extremely difficult for users to maintain the intelligent and targeted level of privacy protection that they need and desire, while simultaneously maintaining their ability to optimally function. Thus, there is a critical need to develop intelligent, automated, and adaptable privacy management systems that can assist users in managing and protecting their sensitive data in the increasingly complex situations and environments that they find themselves in. This work is a first step in exploring the development of such a system, specifically how user personality traits and other characteristics can be used to help automate determination of user sharing preferences for a variety of user data and situations. The Big-Five personality traits of openness, conscientiousness, extroversion, agreeableness, and neuroticism are examined and used as inputs into several popular machine learning algorithms in order to assess their ability to elicit and predict user privacy preferences. Our results show that the Big-Five personality traits can be used to significantly improve the prediction of user privacy preferences in a number of contexts and situations, and so using machine learning approaches to automate the setting of user privacy preferences has the potential to greatly reduce the burden on users while simultaneously improving the accuracy of their privacy preferences and security.

The collaborative nature of content development has given rise to the novel problem of multiple ownership in access control, such that a shared resource is administrated simultaneously by co-owners who may have conflicting privacy preferences and/or sharing needs. Prior work has focused on the design of unsupervised conflict resolution mechanisms. Driven by the need for human consent in organizational settings, this paper explores interactive policy negotiation, an approach complementary to that of prior work. Specifically, we propose an extension of Relationship-Based Access Control (ReBAC) to support multiple ownership, in which a policy negotiation protocol is in place for co-owners to come up with and give consent to an access control policy in a structured manner. During negotiation, the draft policy is assessed by formally defined availability criteria: to the second level of the polynomial hierarchy. We devised two algorithms for verifying policy satisfiability, both employing a modern SAT solver for solving subproblems. The performance is found to be adequate for mid-sized organizations.