New Research on Context Inference of Users’ Social Relationships

Authors:
A. Devlic, R. Reichle, M. Wagner, M. Kirsch Pinheiro, Y. Vanrompay, Y. Berbers, and M. Valla, "Context inference of users’ social relationships and distributed policy management".

Abstract:
Deriving context information without explicit user input is a key requirement for context-aware applications development.

Some information can only be inferred by analyzing the user’s activities over time. An example is social context inference, i.e. deriving social relations from a 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. The derived social relations with the contacts are stored in the Friend of A Friend (FOAF) ontology extended with social relation terms.

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. Thus, there is no need for explicitly stating in the policy all the actors that the policy refers to.

Finally, the article proposes a distributed architecture capable of managing this context information based upon these context access policies.

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