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This paper presents a preliminary investigation on the privacy issues involved in the use of location-based services. It is argued that even if the user identity is not explicitly released to the service provider, the geo-localized history of user-requests can act as a quasi-identifier and may be used to access sensitive information about specific individuals. The paper formally defines a framework to evaluate the risk in revealing a user identity via location information and presents preliminary ideas about algorithms to prevent this to happen.

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Human activity recognition is a challenging problem for context-aware systems and applications. Research in this field has mainly adopted techniques based on supervised learning algorithms, but these systems suffer from scalability issues with respect to the number of considered activities and contextual data. In this paper, we propose a solution based on the use of ontologies and ontological reasoning combined with statistical inferencing. Structured symbolic knowledge about the environment surrounding the user allows the recognition system to infer which activities among the candidates identified by statistical methods are more likely to be the actual activity that the user is performing. Ontological reasoning is also integrated with statistical methods to recognize complex activities that cannot be derived by statistical methods alone. The effectiveness of the proposed technique is supported by experiments with a complete implementation of the system using commercially available sensors and an Android-based handheld device as the host for the main activity recognition module.

Access control models, such as the ones supported by commercial DBMSs, are not yet able to fully meet many application needs. An important requirement derives from the temporal dimension that permissions have in many real-world situations. Permissions are often limited in time or may hold only for specific periods of time. In this article, we present an access control model in which periodic temporal intervals are associated with authorizations. An authorization is automatically granted in the specified intervals and revoked when such intervals expire. Deductive temporal rules with periodicity and order constraints are provided to derive new authorizations based on the presence or absence of other authorizations in specific periods of time. We provide a solution to the problem of ensuring the uniqueness of the global set of valid authorizations derivable at each instant, and we propose an algorithm to compute this set. Moreover, we address issues related to the efficiency of access control by adopting a materialization approach. The resulting model provides a high degree of flexibility and supports the specification of several protection requirements that cannot be expressed in traditional access control models.