Specification of Evolving Privacy Policies for Online Social Networks
Paper in proceeding, 2016

Online Social Networks are ubiquitous, bringing not only numerous new possibilities but also big threats and challenges. Privacy is one of them. Most social networks today offer a limited set of (static) privacy settings, not being able to express dynamic policies. For instance, users might decide to protect their location during the night, or share information with difference audiences depending on their current position. In this paper we introduce T FPPF, a formal framework to express, and reason about, dynamic (and recurrent) privacy policies that are activated or deactivated by context (events) or time. Besides a formal policy language (T PPL), the framework includes a knowledge-based logic extended with (linear) temporal operators and a learning modality (T KBL). Policies, and formulae in the logic, are interpreted over (timed) traces representing the evolution of the social network. We prove that checking privacy policy conformance, and the model-checking problem for T KBL, are both decidable.


Electrical &

Computer Science


Information Systems



Raul Pardo Jimenez

Chalmers, Computer Science and Engineering (Chalmers), Software Technology (Chalmers)

I. Kellyerova

Chalmers, Computer Science and Engineering (Chalmers)

C. Sanchez

IMDEA Institute

Gerardo Schneider

Chalmers, Computer Science and Engineering (Chalmers)

University of Gothenburg

Proceedings 23rd International Symposium on Temporal Representation and Reasoning - Time 2016


Subject Categories

Computer and Information Science



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