Resilient Reputation and Trust Management: Models and Techniques
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The continued advances in service-oriented computing and global communications have created a strong technology push for online information sharing and business transactions among enterprises, organizations and individuals. While these communities offer enormous opportunities, they also present potential threats due to a lack of trust. Reputation systems provide a way for building trust through social control by harnessing the community knowledge in the form of feedback. Although feedback-based reputation systems help community participants decide who to trust and encourage trustworthy behavior, they also introduce vulnerabilities due to potential manipulations by dishonest or malicious players. Therefore, building an effective and resilient reputation system remains a big challenge for the wide deployment of service-oriented computing. This dissertation proposes a decentralized reputation based trust supporting framework called PeerTrust, focusing on models and techniques for resilient reputation management against feedback aggregation related vulnerabilities, especially feedback sparsity with potential feedback manipulation, feedback oscillation, and loss of feedback privacy. This dissertation research has made three unique contributions for building a resilient decentralized reputation system. First, we develop a core reputation model with important trust parameters and a coherent trust metric for quantifying and comparing the trustworthiness of participants. We develop decentralized strategies for implementing the trust model in an efficient and secure manner. Second, we develop techniques countering potential vulnerabilities associated with feedback aggregation, including a similarity inference scheme to counter feedback sparsity with potential feedback manipulations, and a novel metric based on Proportional, Integral, and Derivative (PID) model to handle strategic oscillating behavior of participants. Third but not the least, we develop privacy-conscious trust management models and techniques to address the loss of feedback privacy. We develop a set of novel probabilistic decentralized privacy-preserving computation protocols for important primitive operations. We show how feedback aggregation can be divided into individual steps that utilize above primitive protocols through an example reputation algorithm based on kNN classification. We perform experimental evaluations for each of the schemes we proposed and show the feasibility, effectiveness, and cost of our approach. The PeerTrust framework presents an important step forward with respect to developing attack-resilient reputation trust systems.