EigenTrust++: Attack Resilient Trust Management

Fan, Xinxin; Liu, Ling; Li, Mingchu; Su, Zhiyuan

doi:10.4108/icst.collaboratecom.2012.250420

Cited by 16 publications

(13 citation statements)

References 16 publications

(30 reference statements)

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“…Therefore, we consider the views of the friends of node i about node j based on which it evaluates node j, which is represented by x ij ∈ [0, 1]. Each view of a friend of node i plays a role in evaluation of node j's status as a trusted or untrusted node, therefore, similar to [46] we derive a matrix AX such that:…”

Section: Reputation Calculationmentioning

confidence: 99%

Emergency warning messages dissemination in vehicular social networks: A trust based scheme

Ullah

Kong

Ning

et al. 2020

Vehicular Communications

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To ensure users' safety on the road, a plethora of dissemination schemes for Emergency Warning Messages (EWMs) have been proposed in vehicular networks. However, the issue of false alarms triggered by malicious users still poses serious challenges, such as disruption of vehicular traffic especially on highways leading to precarious effects. This paper proposes a novel Trust based Dissemination Scheme (TDS) for EWMs in Vehicular Social Networks (VSNs) to solve the aforementioned issue. To ensure the authenticity of EWMs, we exploit the user-post credibility network for identifying true and false alarms. Moreover, we develop a reputation mechanism by calculating a trust-score for each node based on its social-utility, behavior, and contribution in the network. We utilize the hybrid architecture of VSNs by employing social-groups based dissemination in Vehicle-to-Infrastructure (V2I) mode, whereas nodes' friendship-network in Vehicle-to-Vehicle (V2V) mode.

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Section: Reputation Calculationmentioning

confidence: 99%

Emergency warning messages dissemination in vehicular social networks: A trust based scheme

Ullah

Kong

Ning

et al. 2020

Vehicular Communications

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“…Nevertheless, the emergence of strategically mischievous participants (SMPs) breaks this routine feedback pattern, i.e. the SMPs on the one hand provide high-quality transaction queries to get honest (positive) ratings from other service receivers as server participants, but oppositely give dishonest (negative) feedback ratings to other service providers as client participants ignoring whatever the transaction queries are satisfied or unsatisfied [Fan et al 2012[Fan et al , 2017Kamvar et al 2003;Su et al 2013]. In addition, the query transaction may fail with non-response, or delivering faulty/low-quality results due to unintended reasons, such as network bandwidth jitter, coolinginduced cloud server downtime, etc.…”

Section: Direct Trust With Local Trust Aggregationmentioning

confidence: 99%

“…For example, the pioneering trust metric Eigen-Trust [Kamvar et al 2003] defined the direct trust for a pair of transacted participants using binary rating {-1, +1}, tr (p i , p j )=-1 denoted a negative rating from p i to p j , and tr (p i , p j )=+1 represented a positive rating. 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.…”

Section: Direct Trust With Local Trust Aggregationmentioning

confidence: 99%

“…For a pair of participants, Hu et al [2008] defined feedback credibility by multiplying their transaction density factor and difference of ratings, then aggregated global trust through power-iteration-based matrix computation. EigenTrust ++ [Fan et al 2012] utilized the pairwise similarity as feedback credibility to weight raw direct trust for further global trust aggregation via UDTP kernel. ServiceTrust [Su et al 2013] defined positive similarity and negative similarity to merge the feedback credibility, subsequently aggregated global trust via UDTP kernel.…”

Section: Summary Of a Ack Analysismentioning

confidence: 99%

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Decentralized Trust Management

et al. 2020

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Decentralized trust management is used as a referral benchmark for assisting decision making by human or intelligence machines in open collaborative systems. During any given period of time, each participant may only interact with a few of other participants. Simply relying on direct trust may frequently resort to random team formation. Thus, trust aggregation becomes critical. It can leverage decentralized trust management to learn about indirect trust of every participant based on past transaction experiences. This paper presents alternative designs of decentralized trust management and their efficiency and robustness from three perspectives. First, we study the risk factors and adverse effects of six common threat models. Second, we review the representative trust aggregation models and trust metrics. Third, we present an in-depth analysis and comparison of these reference trust aggregation methods with respect to effectiveness and robustness. We show our comparative study results through formal analysis and experimental evaluation. This comprehensive study advances the understanding of adverse effects of present and future threats and the robustness of different trust metrics. It may also serve as a guideline for research and development of next generation trust aggregation algorithms and services in the anticipation of risk factors and mischievous threats.

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“…With these observations in mind, we revise formula (2) by formula (3). Let be the probability of a participant choosing to trust only some pre-trusted peers.…”

Section: ) Computing Global Trust By Trust Propagation Kernelmentioning

confidence: 99%

ServiceTrust: Trust Management in Service Provision Networks

Liu

et al. 2013

2013 IEEE International Conference on Services Computing

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Abstract-Service provision networks are popular platforms for decentralized service management. eBay and Amazon are two representative examples of enabling and hosting service provision networks for their customers. Trust management is a critical component for scaling service provision networks to larger set of participants. This paper presents ServiceTrust, a quality sensitive and attack resilient trust management facility for service provision networks. ServiceTrust has three unique features. First, it encapsulates quality-sensitive feedbacks by multi-scale rating scheme and incorporates the variances of user's behaviors into the local trust algorithm. Second, ServiceTrust measures the similarity of two users' feedback behavior and aggregate the local trust values into the global trust algorithm by exploiting pairwise feedback similarity scores to weight the contributions of local trust values towards the global trust of a participant. Finally, pairwise feedback similarity weighted trust propagation is utilized to further strengthen the robustness of global trust computation against malicious or sparse feedbacks. Experimental evaluation with independent and colluding attack models show that ServiceTrust is highly resilient to various attacks and highly effective compared to EigenTrust, one of the most popular and representative trust models to date.

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EigenTrust++: Attack Resilient Trust Management

Cited by 16 publications

References 16 publications

Emergency warning messages dissemination in vehicular social networks: A trust based scheme

Emergency warning messages dissemination in vehicular social networks: A trust based scheme

Decentralized Trust Management

ServiceTrust: Trust Management in Service Provision Networks

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