{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/29534"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/29534","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Reputation-based Trust Framework for Service Oriented Environments","abstract":"We investigate the problem of establishing trust in service-oriented environments. We focus on providing a reputation framework that would enable trust-based interactions with and amongst Web services. We define methods for the creation of reputation information, its collection, and assessment that are robust in the face of a variety of attacks. Our framework (denoted RATEWeb) supports a cooperative model in which Web services share their experiences of the service providers with their peers through feedback ratings. The different ratings are aggregated to derive a service provider's reputation. This in turn is used to evaluate trust. For situations where rater feedbacks are scarce, we use statistical forecasting (particularly, a Hidden Markov Model) to ascertain trust. The approaches and techniques developed under the RATEWeb framework facilitate the optimal selection and/or composition of Web services based on service reputations. We conduct an extensive performance study (analytical and experimental) to assess the fairness and accuracy of the proposed techniques.","abstract_html":"We investigate the problem of establishing trust in service-oriented environments. We focus on providing a reputation framework that would enable trust-based interactions with and amongst Web services. We define methods for the creation of reputation information, its collection, and assessment that are robust in the face of a variety of attacks. Our framework (denoted RATEWeb) supports a cooperative model in which Web services share their experiences of the service providers with their peers through feedback ratings. The different ratings are aggregated to derive a service provider's reputation. This in turn is used to evaluate trust. For situations where rater feedbacks are scarce, we use statistical forecasting (particularly, a Hidden Markov Model) to ascertain trust. The approaches and techniques developed under the RATEWeb framework facilitate the optimal selection and/or composition of Web services based on service reputations. We conduct an extensive performance study (analytical and experimental) to assess the fairness and accuracy of the proposed techniques.","abstract_has_math":false,"creators":["Malik, Zaki"],"institution":"Virginia Tech","degree_name":"Ph. 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