A Real-Time Extension of the Formal Privacy Policy Framework

Abstract

Online social networks (OSNs) have become an important part of people’s lives worldwide. Although users supply OSNs with large amounts of personal data, the ability to control the audience of one’s own information is often limited to a number of predefined and often unclear options. In this work, we introduce two formal frameworks, T FPPF and RT FPPF, with the aim to develop a time-sensitive formalization of evolving OSNs and the ways information spreads in them. Both frameworks comprise three main components. First, a social network model is introduced to capture an OSN, together with its users, the relationships between them, and their knowledge bases, in a specific moment in time. Then, we define the syntax and semantics of a temporal knowledge-based logic used to reason about knowledge and learning in sequences of social network models representing the evolution of an OSN. Finally, we define a formal privacy policy language, powered by the knowledge-based logic, along with a conformance relation that determines whether a policy is violated in a specific OSN. T FPPF and RT FPPF differ in the notion of time they use. T FPPF utilizes the standard and temporal operators and is thus on the logic level able to reason about time in a relative way. On the level of the privacy policy language, it enables to write policies to be enforced in fixed time windows. On the other hand, RT FPPF uses timestamps as a syntactic component on the logic level, allowing for more complex formulae and privacy policies. Both frameworks allow users to define fine-grained, time-sensitive privacy policies based on formal logic, thus addressing the problem of privacy policy ambiguity in OSNs. Moreover, the logic in each framework can also be used directly to reason about knowledge in dynamic OSNs. Both frameworks constitute a step forward in the area of OSN formalizations.