Reliable and Resilient Trust Management in Distributed Service Provision Networks

Abstract

Distributed service networks are popular platforms for service providers to offer services to consumers and for service consumers to acquire services from unknown parties. eBay and Amazon are two well-known examples of enabling and hosting such service networks to connect service providers to service consumers. Trust management is a critical component for scaling such distributed service networks to a large and growing number of participants. In this paper, we present ServiceTrust ++ , a feedback quality sensitive and attack resilient trust management scheme for empowering distributed service networks with effective trust management capability. Comparing with existing trust models, ServiceTrust ++ has several novel features. First, we present six attack models to capture both independent and colluding attacks with malicious cliques, malicious spies and malicious camouflages. Second, we aggregate the feedback ratings based on the variances of participants' feedback behaviors and incorporate feedback similarity as weight into the local trust algorithm. Third, we compute the global trust of a participant by employing conditional trust propagation based on feedback similarity threshold. This allows ServiceTrust ++ to control and prevent malicious spies and malicious camouflage peers to boost their global trust scores by manipulating the feedback ratings of good peers and by taking advantage of the uniform trust propagation. Finally, we systematically combine trust decaying strategy with threshold-value based conditional trust propagation to further strengthen the robustness of our global trust computation against sophisticated malicious feedbacks. Experimental evaluation with both simulation-based networks and real network dataset Epinion show that ServiceTrust ++ is highly resilient against all six attack models and highly effective compared to EigenTrust, the most popular and representative trust propagation model to date.

TrustyFeer: A Subjective Logic Trust Model for Smart City Peer‐to‐Peer Federated Clouds

“…We also compare the results against two state-of-the-art reputation systems, EigenTrust [30] and TNA-SL [28]. The reason for choosing these two systems is because they are the most known trust management systems and many recent proposed systems were based on them [31][32][33][34][35]; in addition, they are already implemented in the simulation tool [36] that we used in our evaluation. Based on the results, we found that our proposed system achieves a better performance in varying the number of CSPs and malicious CSPs in the system.…”

Decentralized Trust Management

“…The above deduces the attack cost in theory, we next verify our argument through performing a set of experiments using popular trust metrics: BetaTrust [Jøsang and Ismail 2002], EigenTrust [Kamvar et al 2003], ServiceTrust [Su et al 2013] and ServiceTrust ++ [Su et al 2015]. To fairly evaluate the attack cost, we set same experiment environment as reported in EigenTrust [Kamvar et al 2003], i.e., the experiment platform includes 60 good participants and 40 mischievous participants.…”

“…The heritage EigenTrust ++ [Fan et al 2012] and GroupTrust [Fan et al 2017], both employed this kind of binary rating. Differently, ServiceTrust [Su et al 2013] and ServiceTrust ++ [Su et al 2015] utilized the multiscale rating {-1, 0, 1, 2, 3, 4, 5} indicating bad, no-rating, neutral, fair, good, very good and excellent query transaction respectively.…”

“…The primary improvement of ServiceTrust ++ [Su et al 2015] over ServiceTrust [Su et al 2013] is the trust propagation kernel. Put differently, the UDTP kernel based ServiceTrust [Su et al 2013] cannot prevent the camouflage participants obtaining positive global trust, but the TCTP kernel based ServiceTrust ++ [Su et al 2015] can throughout block trust propagation through the system-inferred threshold.…”

“…For Threat Models A and B, most trust metrics are effective deriving from the zero-value direct trust from good individuals to mischievous ones, which leads to zero global trust for mischievous participants, such as EigenTrust [Kamvar et al 2003], PeerTrustTVM [Xiong and Liu 2004], PeerTrustPSM [Xiong and Liu 2004], ServiceTrust [Su et al 2013] and ServiceTrust ++ [Su et al 2015]. However, apart from the random trust metric NoneTrust which randomly selects transacted participants, the RTCP trust metric AdaptiveTrust [Chen et al 2016] and RNP trust metric Beta-Trust [Jøsang and Ismail 2002] cannot conquer the two simple attacks.…”