Context inference of users´ social relationships and distributed policy management

Inference of high-level context is becoming crucial in development of context-aware applications. An example is social context inference - i.e., deriving social relations based upon the user´s daily communication with other people. The efficiency of this mechanism mainly depends on the method(s) used to draw inferences based on existing evidence and sample information, such as a training data. Our approach uses rule-based data mining, Bayesian network inference, and user feedback to compute the probabilities of another user being in the specific social relationship with a user whose daily communication is logged by a mobile phone. In addition, a privacy mechanism is required to ensure the user´s personal integrity and privacy when sharing this user´s sensitive context data. Therefore, the derived social relations are used to define a user´s policies for context access control, which grant the restricted context information scope depending on the user´s current context. Finally, we propose a distributed architecture capable of managing this context information based upon these context access policies.