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Trustworthiness and Quality of Context Information
- Young Computer Scientists,. ICYCS
, 2008
"... Context-aware service platforms use context information to customize their services to the current users ’ situation. Due to technical limitations in sensors and context reason-ing algorithms, context information does not always rep-resent accurately the reality, and Quality of Context (QoC) models ..."
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Context-aware service platforms use context information to customize their services to the current users ’ situation. Due to technical limitations in sensors and context reason-ing algorithms, context information does not always rep-resent 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 in-stances 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 pro-pose a QoC model and management architecture that sup-ports the management of QoC trustworthiness and also con-tributes to the terminology alignment of existing QoC mod-els. In our QoC model, trustworthiness is a measurement of the reliability of a context information provider to pro-vide 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 con-text 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. 1
Quality of Context: Models and Applications for Context-aware Systems in Pervasive Environments
- The Knowledge Engineering Review, Special Issue on Web and Mobile Information Services
, 2011
"... Limitations of sensors and the situation of a specific measurement can affect the quality of context information that is implicitly collected in pervasive environments. The lack of information about Quality of Context (QoC) can result in degraded performance of context-aware systems in pervasive env ..."
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Limitations of sensors and the situation of a specific measurement can affect the quality of context information that is implicitly collected in pervasive environments. The lack of information about Quality of Context (QoC) can result in degraded performance of context-aware systems in pervasive environments, without knowing the actual problem. Context-aware systems can take advantage of QoC if context producers also provide QoC metrics along with context information. In this paper, we analyze QoC and present our model for processing QoC metrics. We evaluate QoC metrics considering the capabilities of sensors, circumstances of specific measurement, requirements of context consumer, and the situation of the use of context information. We also illustrate how QoC metrics can facilitate in enhancing the effectiveness and efficiency of different tasks performed by a system to provide context information in pervasive environments. Key-words: Quality of Context, context-aware systems, pervasive environments. 1
Refining the trustworthiness assessment of suppliers through extraction of stereotypes
- In Proceedings of the International Conference on Enterprise Information Systems (ICEIS
, 2010
"... Abstract: Trust management is nowadays considered a promising enabler technology to extend the automation of the supply chain to the search, evaluation and selection of suppliers located world-wide. Current agent-based Computational Trust and Reputation (CTR) systems concern the representation, diss ..."
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Abstract: Trust management is nowadays considered a promising enabler technology to extend the automation of the supply chain to the search, evaluation and selection of suppliers located world-wide. Current agent-based Computational Trust and Reputation (CTR) systems concern the representation, dissemination and aggregation of trust evidences for trustworthiness assessment, and some recent proposals are moving towards situation-aware solutions that allow the estimation of trust when the information about a given supplier is scarce or even null. However, these enhanced, situation-aware proposals rely on ontology-like techniques that are not fine grained enough to detect light, but relevant, tendencies on supplier’s behaviour. In this paper, we propose a technique that allows the extraction of positive and negative tendencies of suppliers in the fulfilment of established contracts. This technique can be used with any of the existing “traditional ” CTR systems, improving their ability in selectively selecting a partner based on the characteristics of the situation in evaluation. In this paper, we test our proposal using an aggregation engine that embeds important properties of the dynamics of trust building. 1
A Situation-Aware Computational Trust Model for Selecting Partners
"... Abstract. Trust estimation is a fundamental process in several multi-agent systems domains, from social networks to electronic business sce-narios. However, the majority of current computational trust systems is still too simplistic and is not situation-aware, jeopardizing the accu-racy of the predi ..."
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Abstract. Trust estimation is a fundamental process in several multi-agent systems domains, from social networks to electronic business sce-narios. However, the majority of current computational trust systems is still too simplistic and is not situation-aware, jeopardizing the accu-racy of the predicted trustworthiness values of agents. In this paper, we address the inclusion of context in the trust management process. We first overview recently proposed situation-aware trust models, all based on the predefinition of similarity measures between situations. Then, we present our computational trust model, and we focus on Contextual Fit-ness, a component of the model that adds a contextual dimensional to existing trust aggregation engines. This is a dynamic and incremental technique that extracts tendencies of behavior from the agents in eval-uation and that does not imply the predefinition of similarity measures between contexts. Finally, we evaluate our trust model and compare it with other trust approaches in an agent-based, open market trading sim-ulation scenario. The results obtained show that our dynamic and incre-mental technique outperforms the other approaches in open and dynamic environments. By analyzing examples derived from the experiments, we show why our technique get better results than situation-aware trust models that are based on predefined similarity measures.
Sinderen, “An information model and architecture for context-aware management domains
- in POLICY. IEEE Computer Society
"... Context-aware service platforms use context-aware policy management solutions to manage user’s 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 ..."
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Context-aware service platforms use context-aware policy management solutions to manage user’s 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. 1
Extracting Trustworthiness Tendencies Using the Frequency Increase Metric, Enterprise Information Systems
- LNBIP, 2011, Volume 73, Part 3, 208Joana Urbano, Ana Paula Rocha, Eugénio Oliveira, "A Trust Aggregation Engine that Uses Contextual Information", EUMAS 2010- 7th European Workshop on Multi-Agent Systems, 12pp., Ayia
, 2009
"... Abstract. Computational trust systems are currently considered enabler tools for the automation and the general acceptance of global electronic business-to-business processes, such as the sourcing and the selection of business partners outside the sphere of relationships of the selector. However, mo ..."
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Abstract. Computational trust systems are currently considered enabler tools for the automation and the general acceptance of global electronic business-to-business processes, such as the sourcing and the selection of business partners outside the sphere of relationships of the selector. However, most of the exist-ing trust models use simple statistical techniques to aggregate trust evidences into trustworthiness scores, and do not take context into consideration. In this paper we propose a situation-aware trust model composed of two components: Sinalpha, an aggregator engine that embeds properties of the dynamics of trust; and CF, a technique that extracts failure tendencies of agents from the history of their past events, complementing the value derived from Sinalpha with con-textual information. We experimentally compared our trust model with and without the CF technique. The results obtained allow us to conclude that the consideration of context is of vital importance in order to perform more accurate selection decisions.
E.: Trust estimation using contextual fitness
, 2010
"... Abstract. Trust estimation is an essential process in several multi-agent systems domains. Although it is generally accepted that trust is situational, the majority of the Computational Trust and Reputation (CTR) systems existing today are not situation-aware. In this paper, we address the inclusion ..."
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Abstract. Trust estimation is an essential process in several multi-agent systems domains. Although it is generally accepted that trust is situational, the majority of the Computational Trust and Reputation (CTR) systems existing today are not situation-aware. In this paper, we address the inclusion of the context in the trust management process. We first refer the benefits of considering context and make an overview of recently proposed situational-aware trust models. Then, we propose Contextual Fitness, a CTR component that brings context into the loop of trust management. We empirically show that this component optimizes the estimation of trustworthiness values in context-specific scenarios. Finally, we compare Contextual Fitness with another situation-aware trust approach proposed in the literature.
DOI: 10.1017/S000000000000000 Printed in the United Kingdom Quality of Context: Models and Applications for Context-aware Systems in Pervasive Environments
"... Limitations of sensors and the situation of a specific measurement can affect the quality of context information that is implicitly collected in pervasive environments. The lack of information about Quality of Context (QoC) can result in degraded performance of context-aware systems in pervasive env ..."
Abstract
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Limitations of sensors and the situation of a specific measurement can affect the quality of context information that is implicitly collected in pervasive environments. The lack of information about Quality of Context (QoC) can result in degraded performance of context-aware systems in pervasive environments, without knowing the actual problem. Context-aware systems can take advantage of QoC if context producers also provide QoC metrics along with context information. In this paper, we analyze QoC and present our model for processing QoC metrics. We evaluate QoC metrics considering the capabilities of sensors, circumstances of specific measurement, requirements of context consumer, and the situation of the use of context information. We also illustrate how QoC metrics can facilitate in enhancing the effectiveness and efficiency of different tasks performed by a system to provide context information in pervasive environments. Key-words: Quality of Context, context-aware systems, pervasive environments. 1
Prediction of User’s Trustworthiness inWeb-based Social Networks
, 2012
"... In Social networks, users need a proper estimation of trust in others to be able to initialize reliable relationships. Some trust evaluation mechanisms have been offered, which use direct ratings to calculate or propagate trust values. However, in some web-based social networks where users only have ..."
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In Social networks, users need a proper estimation of trust in others to be able to initialize reliable relationships. Some trust evaluation mechanisms have been offered, which use direct ratings to calculate or propagate trust values. However, in some web-based social networks where users only have binary relationships, there is no direct rating available. Therefore, a new method is required to infer trust values in these networks. To bridge this gap, this paper aims to propose a new method which takes advantage of user similarity to predict trust values without any need for direct ratings. In this approach, which is based on socio-psychological studies, user similarity is calculated from the profile information and the texts shared by the users via text-mining techniques. Applying Ziegler ratios to our approach revealed that users are more than 50 % more similar to their trusted agents than to arbitrary peers, which proves the validity of the original idea of the study about inferring trust from language similarity. In addition, comparing the real assigned ratings, gathered directly from users, with the experimental results indicated that the predicted trust values are sufficiently acceptable (with a precision of 61%). We have also studied the benefits of using context in inferring trust. In this regard, the analysis revealed that the precision of the predictions can be improved up to 72%. Besides the application of this approach in web-based social networks, the proposed technique can also be of much help in any direct rating mechanism to evaluate the correctness of trust values assigned by users, and increases the robustness of trust and reputation mechanisms against possible security threats. c © 2013 ISC. All rights reserved.
