A Probabilistic Trust Model for Handling Inaccurate Reputation Sources

Patel, Jigar; Teacy, W. T. Luke; Jennings, Nicholas R.; Lück, Michael

doi:10.1007/11429760_14

Cited by 89 publications

(63 citation statements)

References 6 publications

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“…We have chosen eleven systems for our analysis: eBay [4], Unitec [7], FuzzyTrust [15], REGRET [14], NICE [9], Managing the Dynamic Nature of Trust (MDNT) [16], PeerTrust [17], Managing Trust [1], Maximum Likelihood Estimation of Peers' Performance (MLE) [3], EigenTrust [6] and Travos [11]. The systems represent a wide range of applications with different requirements for reputation analysis.…”

Section: Compared Reputation Systemsmentioning

confidence: 99%

Reputation Management Survey

Ruohomaa

Kutvonen

Koutrouli

2007

The Second International Conference on Availability, Reliability and Security (ARES'07)

101

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Electronic markets, distributed peer-to-peer applications and other forms of online collaboration are all based on mutual trust, which enables transacting peers to overcome the uncertainty and risk inherent in the environment. Reputation systems provide essential input for computational trust as predictions on future behaviour based on the past actions of a peer. In order to analyze the maturity of current reputation systems, we compare eleven reputation systems within a taxonomy of the credibility aspects of a reputation system. The taxonomy covers three topics: 1) the creation and content of a recommendation, 2) the selection and use of recommenders, and 3) the interpretation and reasoning applied to the gathered information. Although we find it possible to form a trusted reputation management network over an open network environment, there are still many regulatory and technical obstacles to address. This survey reveals various good mechanisms and methods used, but the area still requires both a) formation of standard mechanisms and metrics for reputation system collaboration and b) standard metainformation of right granularity for evaluating the credibility of reputation information provided.

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Section: Compared Reputation Systemsmentioning

confidence: 99%

Reputation Management Survey

Ruohomaa

Kutvonen

Koutrouli

2007

The Second International Conference on Availability, Reliability and Security (ARES'07)

101

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“…In this structure of web, after gathering the claims of resource providers, the mechanism of trust evaluation is used. In this group of trust models, reputation of an entity is used to evaluate the trustworthiness [1], [7], [14]. With the emersion of semantic web, resources are defined and represented based on their constituent factors [4], [9], [15].…”

Section: Trust Evaluation Processmentioning

confidence: 99%

Applying AHP Technique for Trust Evaluation in the Semantic Web

Cami¹,

Amiri²

2013

IJMLC

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Abstract-The increasing reliance on information gathered from the web and other internet technologies raises the issue of trust. Through the development of semantic Web,One major difficulty is that, by its very nature, the semantic web is a large, uncensored system to which anyone may contribute. This raises the question of how much credence to give each resource. We can't expect eachuser to know the trustworthiness of each resource, nor would we want to assign top-down or global credibility values due to the subjective nature of trust. Trust policiesand trust evaluation mechanisms are needed to filter untrustworthy resource. We tackle this problem by employing a trust model for evaluating trustworthiness ofeach resource. This proposed model uses semantic webmetadata, recommendation, and reputation as based factorfor evaluation algorithm. The weighting and Combination Methods are two main challenges for Proposed Trust evaluation algorithm. These factors have various type and semantic. Therefore we apply the AHP technique for trust evaluation that offers justification for trust decisions andcontrolled trust measurement.Index Terms-AHP, content trust, hybrid trust model, semantic web, trust.

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“…This assumption is not solid in open environments where recommendations can be very few in number, most of which can be untruthful. A variant of endogenous approach is used in [24], where each entity records all the ratings and subsequent interaction experiences. Assume node a receives a recommendation from recommender r, a first picks out all the entities whom r has recommended with a similar value (e.g., within the range [a..b]).…”

Section: Related Workmentioning

confidence: 99%

An incentive compatible reputation mechanism for ubiquitous computing environments

n¹,

Issarny

2007

Int. J. Inf. Secur.

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The vision of ubiquitous computing is becoming a reality thanks to the advent of portable devices and the advances in wireless networking technologies. It aims to facilitate user tasks through seamless utilization of services available in the surrounding environments. In such distributed environments featuring openness, interactions, especially service provision and consumption, between entities that are unknown or barely known to each other, are commonplace. Trust management through reputation mechanism to facilitate such interactions is recognized as an important element of ubiquitous computing. It is, however, faced by the problems of how to stimulate reputation information sharing and honest recommendation elicitation. We present in this paper an incentive compatible reputation mechanism to facilitate the trustworthiness evaluation in ubiquitous computing environments. It is based on probability theory and supports reputation evolution and propagation. Our reputation mechanism not only shows robustness against lies, but also stimulates honest and active recommendations. The latter is realized by ensuring that active and honest recommenders, compared to inactive or dishonest ones, can elicit the most honest (helpful) recommendations and thus suffer the least number of wrong trust decisions, as validated by simulation based evaluation.

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A Probabilistic Trust Model for Handling Inaccurate Reputation Sources

Cited by 89 publications

References 6 publications

Reputation Management Survey

Reputation Management Survey

Applying AHP Technique for Trust Evaluation in the Semantic Web

An incentive compatible reputation mechanism for ubiquitous computing environments

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