Decoupling Service and Feedback Trust in a Peer-to-Peer Reputation System

Swamynathan, Gayatri; Zhao, Ben Y.; Almeroth, Kevin C.

doi:10.1007/11576259_10

Cited by 25 publications

(13 citation statements)

References 11 publications

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“…A lattice structure with trust and knowledge axis is used to model various trusting conditions. Swamynathan et al [41] decouple trust metrics on service and recommendation contexts to assess trustworthiness better. Creating contexts of trust can be helpful to address issues in various domains.…”

Section: Related Workmentioning

confidence: 99%

SORT: A Self-ORganizing Trust Model for Peer-to-Peer Systems

Can

Bhargava

2013

IEEE Trans. Dependable and Secure Comput.

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Abstract-Open nature of peer-to-peer systems exposes them to malicious activity. Building trust relationships among peers can mitigate attacks of malicious peers. This paper presents distributed algorithms that enable a peer to reason about trustworthiness of other peers based on past interactions and recommendations. Peers create their own trust network in their proximity by using local information available and do not try to learn global trust information. Two contexts of trust, service, and recommendation contexts, are defined to measure trustworthiness in providing services and giving recommendations. Interactions and recommendations are evaluated based on importance, recentness, and peer satisfaction parameters. Additionally, recommender's trustworthiness and confidence about a recommendation are considered while evaluating recommendations. Simulation experiments on a file sharing application show that the proposed model can mitigate attacks on 16 different malicious behavior models. In the experiments, good peers were able to form trust relationships in their proximity and isolate malicious peers.

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Section: Related Workmentioning

confidence: 99%

SORT: A Self-ORganizing Trust Model for Peer-to-Peer Systems

Can

Bhargava

2013

IEEE Trans. Dependable and Secure Comput.

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“…Notice that SSON is constructed to promote correct stereotypes sharing to help inexperienced agents estimate trustworthiness of an unknown service provider, but it is not used to discover trustworthy transaction partner because agents who provide high quality service may not necessarily report correct information about other agents (and vice versa) [45,38]. In the scenario that the agents who provide correct stereotypes also act honestly in a transaction, SSON can be used to help promote successful transactions (i.e., select reliable agents who have high trust scores as the service providers).…”

Section: Stereotypes Sharing Overlay Network (Sson)mentioning

confidence: 99%

“…Additionally, if the referral trust is inaccurate, the transitive trust may be erroneous. Furthermore, trust may not be transitive between different contexts: A may trust B in a certain context (e.g., serving good food), but not necessarily to recommend other agents (a "meta"-context, or the referral context) [45,38].…”

Section: Introductionmentioning

confidence: 99%

Trust beyond reputation: A computational trust model based on stereotypes

Liu

Datta

Rządca

2013

Electronic Commerce Research and Applications

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Models of computational trust support users in taking decisions. They are commonly used to guide users' judgements in online auction sites; or to determine quality of contributions in Web 2.0 sites. However, most existing systems require historical information about the past behavior of the specific agent being judged. In contrast, in real life, to anticipate and to predict a stranger's actions in absence of the knowledge of such behavioral history, we often use our "instinct"-essentially stereotypes developed from our past interactions with other "similar" persons. In this paper, we propose StereoTrust, a computational trust model inspired by stereotypes as used in real-life. A stereotype contains certain features of agents and an expected outcome of the transaction. When facing a stranger, an agent derives its trust by aggregating stereotypes matching the stranger's profile. Since stereotypes are formed locally, recommendations stem from the trustor's own personal experiences and perspective. Historical behavioral information, when available, can be used to refine the analysis. According to our experiments using Epinions.com dataset, StereoTrust compares favorably with existing trust models that use different kinds of information and more complete historical information.

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“…Notice that LKSON is constructed to ensure reliable local knowledge sharing to help inexperienced agents estimate trustworthiness of a potential transaction, but is not used to discover trustworthy transaction partner (i.e., LKSON is not a trust overlay network like that in [33]) because it has been reported in related literature [32], [28] that the agents who provide high quality service may not report genuine feedbacks and vice versa due to various reasons. In the case that the agents who provide useful information also act honestly in a transaction, LKSON can be used to help promote successful transactions (i.e., selecting reliable agents who have high trust scores as the service providers).…”

Section: Definition 1 (Local Knowledge Sharing Overlay Network) a Locmentioning

confidence: 99%

A Generic Trust Framework for Large‐scale Open Systems Using Machine Learning

Liu

Trédan

Datta

2013

Computational Intelligence

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In many large scale distributed systems and on the web, agents need to interact with other unknown agents to carry out some tasks or transactions. The ability to reason about and assess the potential risks in carrying out such transactions is essential for providing a safe and reliable environment. A traditional approach to reason about the trustworthiness of a transaction is to determine the trustworthiness of the specific agent involved, derived from the history of its behavior. As a departure from such traditional trust models, we propose a generic, machine learning approach based trust framework where an agent uses its own previous transactions (with other agents) to build a knowledge base, and utilize this to assess the trustworthiness of a transaction based on associated features, which are capable of distinguishing successful transactions from unsuccessful ones. These features are harnessed using appropriate machine learning algorithms to extract relationships between the potential transaction and previous transactions. The trace driven experiments using real auction dataset show that this approach provides good accuracy and is highly efficient compared to other trust mechanisms, especially when historical information of the specific agent is rare, incomplete or inaccurate.

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Decoupling Service and Feedback Trust in a Peer-to-Peer Reputation System

Cited by 25 publications

References 11 publications

SORT: A Self-ORganizing Trust Model for Peer-to-Peer Systems

SORT: A Self-ORganizing Trust Model for Peer-to-Peer Systems

Trust beyond reputation: A computational trust model based on stereotypes

A Generic Trust Framework for Large‐scale Open Systems Using Machine Learning

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