Effective Usage of Computational Trust Models in Rational Environments

Abstract

Computational reputation-based trust models using statistical learning have been intensively studied for distributed systems where peers behave maliciously. However practical applications of such models in environments with both malicious and rational behaviors are still very little understood. In this article, we study the relation between their accuracy measures and their ability to enforce cooperation among participants and discourage selfish behaviors. We provide theoretical results that show the conditions under which cooperation emerges when using computational trust models with a given accuracy, and how cooperation can still be sustained while reducing the cost and accuracy of those models.Specifically, we propose a peer selection protocol that uses a computational trust model as a dishonesty detector to filter out unfair ratings. We prove that such a model with reasonable misclassification error bound in identifying malicious ratings can effectively build trust and cooperation in the system, considering rationality of participants. These results reveal two interesting observations. First, the key to the success of a reputation system in a rational environment is not a sophisticated trust-learning mechanism, but an effective identity-management scheme to prevent whitewashing behaviors. Second, given an appropriate identity-management mechanism, a reputation-based trust model with a moderate accuracy bound can be used to effectively enforce cooperation in systems with both rational and malicious participants. As a result, in heterogeneous environments where peers use different algorithms to detect misbehavior of potential partners, cooperation may still emerge. We verify and extend these theoretical results to a variety of settings involving honest, malicious, and strategic players through extensive simulation. These results will enable a much more targeted, cost-effective and realistic design for decentralized trust management systems, such as needed for peer-to-peer, electronic commerce, or community systems.

Using Identity Premium for Honesty Enforcement and Whitewashing Prevention

“…The misclassification error bounds of these reputation‐based probabilistic trust models are well lower than 0.5 even under various adaptively malicious attacks by participating raters. Other empirical experimental results (Vu and Aberer, ) have confirmed that other computational trust models, such as those proposed by Xiong and Liu (), are also capable of classifying unreliable ratings with a small error bound ϵ under various attack scenarios, and thus, they can be readily used to implement a dishonesty detector.…”

“…In consequence, this may change the incentives for a provider to accept our identity premium mechanism. We have shown in our previous work (Vu and Aberer, ) that honest negative ratings due to unavoidable circumstances have only minor impact on providers.…”

“…The current work is inspired by our previous work (Vu and Aberer, ). More specifically, in the previous work, identities are assumed to be costly, and the provider selection protocol in Definition 3 can simply assure that any rational provider is motivated to cooperate in all but a small number of its last transactions.…”

“…We proved in an early work (Vu and Aberer, ) that if identities are costly, the selection protocol in Definition assures that any rational provider is motivated to cooperate in all but a small number Δ v of its last transactions. This Δ v is dependent on the misclassification error bound ϵ of the trust mechanism being used in the system to detect malicious ratings and the bound v * of the temporary cheating gain of a provider after a transaction.…”

An Auditable Reputation Service for Collective Adaptive Systems

“…Reputation systems can be centralized or distributed [25,26,27,28]. Liu [29] describes criteria for classifying and analysing centralised reputation systems.…”