Emergent Social Rationality in a Peer-to-Peer System

Zhao

et al. 2013

Security Comm Networks

Trust mechanism has become a research focus in recent years as a novel and valid way to ensure the transaction security in peer‐to‐peer file sharing networks. Nevertheless, some fundamental challenges still exist, for example: How can malicious peers be effectively isolated? How can various threats of manipulation by strategic peers be resisted? What strategy should be used to ensure that the service providers are authentic peers? Considering these challenges in our minds, in this paper, we propose a new trust mechanism based on the maximum flow theory. We firstly add a few prestigious peers into a cluster as the original members according to their transaction behaviors in a period; then, we perform maximum flow algorithm and identify those peers that still link from (to) the peers in the cluster as new members, which is carried out repeatedly, and almost every normal peer would finally become the member of the cluster. Each request peer has the priority to select downloading sources from this cluster according to our trust mechanism. In this way, the malicious peers are isolated, and their transaction behaviors are also confined largely even though they have high reputation. Extensive experimental results confirm the efficiency of our trust mechanism against the threats of exaggeration, cheat, collusion, and disguise. Copyright © 2013 John Wiley & Sons, Ltd.

Section: Introductionmentioning

confidence: 99%

Peer cluster: a maximum flow‐based trust mechanism in P2P file sharing networks

Zhao

et al. 2013

Security Comm Networks

“…Most of the existing research has developed trust and reputation computational models by utilizing formal methods, such as fuzzy logic theory [13] [11], Bayesian network [15], subjective logic [4] [5], social cognitive methods [1] [8], and game theory [9] [16]. EigenTrust [7] is one of the most popular reputation management models to date.…”

Section: Introductionmentioning

confidence: 99%

EigenTrust++: Attack Resilient Trust Management

Proceedings of the 8th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing

Liu

et al. 2012

Abstract-This paper argues that trust and reputation models should take into account not only direct experiences (local trust) and experiences from the circle of "friends", but also be attack resilient by design in the presence of dishonest feedbacks and sparse network connectivity. We first revisit EigenTrust, one of the most popular reputation systems to date, and identify the inherent vulnerabilities of EigenTrust in terms of its local trust vector, its global aggregation of local trust values, and its eigenvector based reputation propagating model. Then we present EigenTrust ++ , an attack resilient trust management scheme. EigenTrust ++ extends the eigenvector based reputation propagating model, the core of EigenTrust, and counters each of vulnerabilities identified with alternative methods that are by design more resilient to dishonest feedbacks and sparse network connectivity under four known attack models. We conduct extensive experimental evaluation on EigenTrust ++ , and show that EigenTrust ++ can significantly outperform EigenTrust in terms of both performance and attack resilience in the presence of dishonest feedbacks and sparse network connectivity against four representative attack models.

“…Recently, many researches on reputation mechanism based on various methods, like fuzzy logic theory [4][5][6], Bayesian network [7,8], subjective logic [9,10], social cognitive [11][12][13][14], fined-grained [15,16], and game theory [17][18][19][20], have been proposed in this field on which both academia and industry are concentrating their attention.…”

mentioning

confidence: 99%

Behavior-based reputation management in P2P file-sharing networks

Journal of Computer and System Sciences

et al. 2012

Trust research has become a key issue in the last few years as a novel and valid solution to ensure the security and application in peer-to-peer (P2P) file-sharing networks. The accurate measure of trust and reputation is a hard problem, most of the existing trust mechanisms adopt the historical behavior feedback to compute trust and reputation. Thus exploring the appropriate transaction behavior becomes a fundamental challenge. In P2P system, each peer plays two roles: server and client with responsibility for providing resource service and trust recommending respectively. Considering the resource service behavior and trust recommending behavior of each peer, in this paper, we propose a new trust model adopting the technology to calculate eigenvectors of trust rating and recommending matrices. In our model, we define recommended reputation value to evaluate the resource service behavior, and recommending reputation value to evaluate the trust recommendation behavior. Our algorithm would make these two reputation values established an interrelated relation of reinforcing mutually. The normal peers provide authentic file uploading services, as well as give correct trust recommendation, so they can form a trusted and cooperative transaction community via the mutual reinforcement of recommended and recommending reputation values. In this way, the transaction behaviors of those malicious peers are isolated and confined effectively. Extensive experimental results also confirm the efficiency of our trust model against the threats of exaggeration, collusion, disguise, sybil and single-behavior.