Abstract
The concept of reputation is widely used as a measure of trustworthiness based on ratings from members in a community. The adoption of reputation systems, however, relies on their ability to capture the actual trustworthiness of a target. Several reputation models for aggregating trust information have been proposed in the literature. The choice of model has an impact on the reliability of the aggregated trust information as well as on the procedure used to compute reputations. Two prominent models are flow-based reputation (e.g., EigenTrust, PageRank) and subjective logic-based reputation. Flow-based models provide an automated method to aggregate trust information, but they are not able to express the level of uncertainty in the information. In contrast, subjective logic extends probabilistic models with an explicit notion of uncertainty, but the calculation of reputation depends on the structure of the trust network and often requires information to be discarded. These are severe drawbacks. In this work, we observe that the `opinion discounting' operation in subjective logic has a number of basic problems. We resolve these problems by providing a new discounting operator that describes the flow of evidence from one party to another. The adoption of our discounting rule results in a consistent subjective logic algebra that is entirely based on the handling of evidence. We show that the new algebra enables the construction of an automated reputation assessment procedure for arbitrary trust networks, where the calculation no longer depends on the structure of the network, and does not need to throw away any information. Thus, we obtain the best of both worlds: flow-based reputation and consistent handling of uncertainties.
Highlights
Advances in ICT and the increasing use of the Internet have resulted in changes in the way people do everyday things and interact with each other
We show that Evidence-Based Subjective Logic (EBSL) provides a solid foundation for the development of reputation models able to express the level of confidence in computed reputations
We considered six approaches: (i) the flowbased method without uncertainty in Eq (1); (ii) the flowbased subjective logic (SL) approach presented in Sect. 3; (iii) SL in which the specification of the trust network is transformed to canonical form by removing the edge from 4 to 5 (i.e., A45 is set to U in Eq√. (14)); (iv) EBSL with g(x) = xb; (v) EBSL with g(x) = xb; and (vi) EBSL using the operator
Summary
Advances in ICT and the increasing use of the Internet have resulted in changes in the way people do everyday things and interact with each other. Uncertainty about services and users’ behavior is often perceived as a risk [2] and, it can restrain a user from engaging in a transaction with unknown parties. To fully exploit the potential of online services, platforms and online communities, it is necessary to establish and manage trust among the parties involved in a transaction [11,40,43]. Reputation is widely adopted to build trust among users in online communities where users do not know each other beforehand. Reputation provides an indication of services’ and users’ trustworthiness based on their past behavior [36]. When a user has to decide whether to interact with another party, he can consider its reputation and start the transaction only if it is trustworthy. A reputation system, which helps managing reputations (e.g., by collecting, distributing and aggregating feedback about services and users’ behavior), becomes a fundamental component of the
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