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This paper presents preliminary results on anonymization and obfuscation techniques to preserve users' privacy in context-aware service provisioning. The techniques are based on generalizing request parameters as well as the context data provided to the application. Local context semantic aggregation is used to improve the quality of service that can be achieved while preserving privacy. The paper also shows how the software architecture of the CARE middleware can be extended to implement the proposed techniques

The Context Aggregation and REasoning (CARE) middleware aims at supporting context-aware adaptation of internet services in a mobile computing environment. Context awareness requires the acquisition, representation and processing of information that goes beyond raw context data - like device features, network status and user location - to include semantically rich data such as the current activity and interests of users. Representing and reasoning with the latter class of data require the use of ontologies and ontological reasoning. It is well known that reasoning with ontologies poses significant performance issues. The CARE hybrid reasoning mechanism is based on a loose interaction between ontological reasoning and efficient reasoning in a restricted logic programming language. In this paper we illustrate the hybrid reasoning approach adopted by CARE and report the extensive experimental results on ontology-based context reasoning that support our approach. © 2009 Inderscience Enterprises Ltd.

The CARE middleware aims at supporting context-aware adaptation of Internet services in a mobile computing environment. The CARE hybrid reasoning mechanism is based on a loose interaction between ontological reasoning and efficient reasoning in a restricted logic programming language. In this paper we report recent experimental results on ontology-based context reasoning that support the hybrid approach. © 2007 IEEE.

This paper reports our experience with representing and reasoning with context information within the CARE middleware. CARE was developed to support context-aware service adaptation for mobile users. Expressiveness and computational issues are discussed and the specific solution adopted in CARE is presented

Context awareness is emerging as an essential feature for the next generation of Internet mobile services. Context awareness is a desirable feature for many application areas, including natural language understanding, electronic commerce, telemedicine, and e-learning, just to cite a few. It is, however, particularly relevant for mobile and pervasive computing, as mobile devices naturally enable a much wider set of contexts characterized, among other things, by a spatiotemporal dimension, a wide range of device features and networking capabilities, and very different environment situations. In this chapter, we discuss how the many parameters defining the context of a specific mobile service request can be acquired from different sources, formally represented, managed by software agents, and integrated in a consistent uniform description to be used for service adaptation

Context-awareness in mobile and ubiquitous computing requires the acquisition, representation and processing of context information which is not limited to the device features, network status, or user location but includes semantically rich data like user preferences and user current activity. At the University of Milan, we have designed and implemented a middleware architecture for adapting mobile services accordingly to distributed context information involving all the aspects cited above. The middleware also allows users and service providers to define adaptation policies, in the form of logical rules. The framework adopts a hybrid form of ontological and rule-based reasoning and provides mechanisms to reconcile possibly conflicting context information and policies. The goal of this demonstration is to show the viability of our solution and to show its effectiveness when considering the adaptation of a real mobile service

The heterogeneity of device capabilities, network conditions and user contexts that is associated with mobile computing has emphasized the need for more advanced forms of adaptation of Internet services. This paper presents a framework that addresses this issue by managing distributed profile information and adaptation policies, solving possible conflicts by means of an inference engine and prioritization techniques. The profile information considered in the framework is very broad, including user preferences, device and network capabilities, and user location and context. The framework has been validated by experiments on the efficiency of the proposed conflict resolution mechanism, and by the implementation of the main components of the architecture. The paper also illustrates a specific testbed application in the context of proximity marketing.

Context-awareness in mobile and ubiquitous computing requires the acquisition, representation and processing of information which goes beyond the device features, network status, and user location, to include semantically rich data, like user interests and user current activity. On the other hand, when services have to be provided on-the-fly to many mobile users, the efficiency of reasoning with these data becomes a relevant issue. Experimental evidence has lead us to consider currently impractical a tight integration of ontological reasoning with rule based reasoning at the time of request. This paper illustrates a hybrid approach where ontological reasoning is loosely coupled with the efficient rule-based reasoning of a middleware architecture for service adaptation. While rule-based reasoning is performed at the time of service request to evaluate adaptation policies and reconcile possibly conflicting context information, ontological reasoning is mostly performed asynchronously by local context providers to derive non-shallow context information. A limited form of ontological reasoning is activated at the time of request only when essential for service provisioning.