Rapid advances in computing technology have made it possible to create a pervasive computing environment where computing resources and facilities are available wherever a user may be. This pervasive computing environment goes beyond the concept of city-wide, or nation-wide, wireless network coverage and aims to make interaction between the user and the computing facilities as easy and natural as possible. This requires intelligence on the part of the underlying networks and software supporting the pervasive computing environment, and also awareness of the environment and the context in which the user is operating. Intelligence and awareness on the part of the pervasive computing environment are provided by analysing what is referred to as “context information”. This context information can be obtained from hardware or software sensors in the environment, be based on profiled information or derived from context information already known to the pervasive computing environment. The potential to misuse the pervasive computing infrastructure to violate the privacy of users is significant. These violations can range from trading context information about users without consent to abusing a user’s location information to stalk them. To make the link between users and context information gathered by sensors we developed a concept of contextdependent, multi-party context information ownership. In our approach, ownership of context information entitles the owner(s) to determine how, to whom and under what conditions context information is disclosed to third-party entities. This concept of ownership was then developed into modelling techniques and a representation compatible with a wide range of Object Oriented context modelling approaches. Context information privacy for the users of the pervasive computing environment was then addressed by building on our concept of context-dependent, multi-party context information ownership. Owners of context information, identified using our ownership modelling approach, were able to specify their disclosure preferences for context information modelled as belonging to them. These preferences were captured using a context-dependent privacy preference language. As part of the contribution to the field of context information privacy a context information obfuscation mechanism was also developed. Control of this obfuscation mechanism was integrated with the preference language permitting users to specify not only when, how, why and for what purpose their context information can be disclosed by the pervasive computing environment, but also at what level of detail disclosure may take place. If necessary, the obfuscation mechanism operates on requested context information to reduce its detail level to meet these disclosure requirements. After developing a context information privacy mechanism, we then focused on enhancing the operation of security mechanisms within pervasive computing environments by using context information. From a security perspective, the pervasive computing environment represents a significant challenge as (1) there is no single administrative entity to establish and enforce security policy, (2) nodes are highly mobile and may need to interact with other nodes with which they have no previous relationship and (3) connectivity may be intermittent meaning that trusted infrastructure may not always be available. These features of pervasive computing environments mean that existing approaches to security that rely on the existence of trusted infrastructure, need well-defined network boundaries and require pre-existing relationships between the security service are unable to function. As the security field is very broad, this thesis focused on one aspect: authentication. To facilitate authentication in pervasive computing environments, we developed the Distributed Certification and Authentication Service (DCAS). DCAS acts as a distributed Certification Authority comprised of a group of trustworthy nodes that collaborate to provide the services of a Certification Authority and can be set up on-the-fly to service clusters of isolated nodes. The trusted nodes within DCAS not only issue public key certificates, but also perform authentication of applicant nodes. Applicant nodes are able to authenticate using “traditional authentication factors” (such as public key certificates), as well as context information sensed from the environment, or a combination of both. To demonstrate the viability of DCAS, its core functionality was implemented and deployed on a small IEEE 802.11b network. In order to aid in establishing a DCAS within a network, a probability-based model of DCAS behaviour under certain conditions was created. This model was verified using simulation.
