R. Neisse

Trustworthiness and Quality of Context Information

Context-aware service platforms use context information to customize their services to the current users situation. Due to technical limitations in sensors and context reasoning algorithms, context information does not always represent accurately the reality, and Quality of Context (QoC) models have been proposed to quantify this inaccuracy. The problems we have identified with existing QoC models is that they do not follow a standard terminology and none of them clearly differentiate quality attributes related to instances of context information (e.g. accuracy and precision) from trustworthiness, which is a quality attribute related to the context information provider. In this paper we propose a QoC model and management architecture that supports the management of QoC trustworthiness and also contributes to the terminology alignment of existing QoC models. In our QoC model, trustworthiness is a measurement of the reliability of a context information provider to provide context information about a specific entity according to a certain quality level. This trustworthiness value is used in our QoC management architecture to support context-aware service providers in the selection of trustworthy context providers. As a proof of concept to demonstrate the feasibility of our work we show a prototype implementation of our QoC model and management architecture.

Trust management model and architecture for context-aware service platforms

The entities participating in a context-aware service platform need to establish and manage trust relationships in order to assert different trust aspects including identity provisioning, privacy enforcement, and context information provisioning. Current trust management models address these trust aspects individually when in fact they are dependent on each other. In this paper we identify and analyze the trust relationships in a context-aware service platform and propose an integrated trust management model that supports quantification of trust for different trust aspects. Our model addresses a set of trust aspects that is relevant for our target context-aware service platform and is extensible with other trust aspects. We propose to calculate a resulting trust value for contextaware services, which considers the dependencies between the different trust aspects, and aims to support the users in the selection of the more trustworthy services. In this calculation we target two types of user goals: one with high priority in privacy enforcement (privacy concerned) and one with high priority in the service adaptation (service concerned). Based on our trust model we have designed a distributed trust management architecture and implemented a proof of concept prototype.

An Information Model and Architecture for Context-Aware Management Domains

Context-aware service platforms use context-aware policy management solutions to manage users privacy preferences, to manage trust relationships, and to control access to the platform resources. However, existing context-aware policy management solutions focus on at most one policy management area (e.g. trust management, or privacy, or access control) and are difficult to integrate due to their unrelated policy/context information models and semantics. This leads to an integration problem, and to a policy management nightmare, because context-aware policies of different management areas have to be managed using different tools. In this paper, we address this problem using a new context-aware policy management abstraction called context-aware management domains (CAMDs). CAMDs allow the grouping of entities, for which a common set of policies apply, based on the entities context. In comparison to existing solutions CAMDs provide a more generic context-aware policy management abstraction. CAMDs are suitable for any policy management area, and allow context-aware obligation policies, which are not supported by existing policy management solutions.

Context-Aware trust domains

Context-aware service platforms need to establish and manage trust relationships for users to know if the users privacy policies are being enforced and for service providers to control access to their services. Current trust solutions are not suitable for this because they do not address in an integrated manner trust issues related to identity provisioning, privacy enforcement and context information trustworthiness. In addition, due to their hierarchical and centralized design, current trust management solutions do not scale well in the ad-hoc pervasive environments in which context-aware platforms are typically deployed. In this paper we propose context-aware trust domains as a management solution for context-aware service platforms.

Semantic context reasoning using ontology based models

New mobile computing technologies and the increasing use of portable devices have pushed the development of the so-called context-aware applications. This new class of applications aims at improving human-computer interactions by supporting dynamic adaptations according to context changes. This paper discusses the suitability of using ontologies for modeling context information and presents the design, implementation and applicability of an ontology based context interpreter. The proposed interpreter is responsible for inferring new context information in a context-aware services platform.

Trust management support for context-aware service platforms

High quality context information retrieved from trustworthy context providers allows a more reliable context-aware service adaption but also implies a higher risk for the service users in case of privacy violations. In this chapter we present a trust management model that support users and providers of context-aware services in managing the trade-off between privacy protection and context-based service adaptation. We applied our trust management model in two trust-based selection mechanisms. The first trust-based selection mechanism support users of context-aware services in selecting trustworthy service providers to interact with. This mechanism supports the users in the selection process, taking into account the users’ goals, trust beliefs, and the trust dependencies between the service users and the entities that collaborate in the context-aware service provisioning. The second trust-based selection mechanism supports context-aware service providers in selecting trustworthy context providers taking into account the trustworthiness of the context providers to provide context information about a specific user and quality level. To conclude this chapter we present the evaluation of the technical feasibility of our trustmanagement model in trust-based selection mechanisms through a prototype implementation.