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Abstract

Abstract-The challenge of pervasive computing consists in offering access to computing services anywhere and anytime with any devices. However, before it becomes a reality, the problems of access control and authentication have to be solved, among others. Existing solutions are inadequate without adaptation to this specific environment. Among the promising approaches, the trust paradigm seems to be more flexible than others. We base this proposal on this paradigm to implement a distrust model, so-called APC (Access Pass Certificate). The main objective of this model is to enable authorized user to roam and to access trusted sites though they are not known locally. A user can claim two kinds of APCs provided by two kinds of sites: the home site (where the user has an account) and the trusted site (that trusts the user). Using these certificates, the user can progressively extend her access scope. This model implements a decentralized mapping policy, where the correspondence between the user's home profile and her rights in the trusted sites is determined by the trusted site. This distrust model and its implementation are presented in this article where we exhibit its importance for large but controlled access in pervasive environments.

Interoperability among heterogeneous systems is a key challenge in today's networked environment, which is characterised by continual change in aspects such as mobility and availability. Automated solutions appear then to be the only way to achieve interoperability with the needed level of flexibility and scalability. While necessary, the techniques used to achieve interaction, working from the highest application level to the lowest protocol level, come at a substantial computational cost, especially when checks are performed indiscriminately between systems in unrelated domains. To overcome this, we propose to use machine learning to extract the high-level functionality of a system and thus restrict the scope of detailed analysis to systems likely to be able to interoperate.