A distributed trust model

Abdul-Rahman, Alfarez; Hailes, Stephen

doi:10.1145/283699.283739

Cited by 495 publications

(296 citation statements)

References 6 publications

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“…The framework presented in this paper supports a wide variety of combination functions, such as linear pool [17][18], noisy OR [28], and logistic regression [4]. We view the coefficients in these functions (one per source) as measuring the user's trust in each source, 1 and answer the question: how can a user decide how much to trust a source she does not know directly? Our answer is based on recursively propagating trust: if A has trust u in B and B has trust v in C, then A should have some trust t in C that is a function of u and v. We place restrictions on allowable methods for combining trusts that enable the efficient and local computation of derived trusts.…”

Section: Modelmentioning

confidence: 99%

“…Any user may assert her personal belief in the statement, which is taken from [0,1]. A high value means that the statement is accurate, credible, and/or relevant.…”

Section: Modelmentioning

confidence: 99%

“…Cryptography also makes use of a web of trust to verify identity [10]. In Abdul-Rahman's system, John's trust in Jane, and John's trust in Jane's ability to determine who is trustworthy, are separate, though discrete and only qualitatively valued [1]. Such a separation would be interesting to consider in our framework as well.…”

Section: Related Workmentioning

confidence: 99%

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Trust Management for the Semantic Web

Richardson

Agrawal

Domingos

2003

Lecture Notes in Computer Science

681

379

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Abstract. Though research on the Semantic Web has progressed at a steady pace, its promise has yet to be realized. 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 source. We cannot expect each user to know the trustworthiness of each source, nor would we want to assign top-down or global credibility values due to the subjective nature of trust. We tackle this problem by employing a web of trust, in which each user maintains trusts in a small number of other users. We then compose these trusts into trust values for all other users. The result of our computation is not an agglomerate "trustworthiness" of each user. Instead, each user receives a personalized set of trusts, which may vary widely from person to person. We define properties for combination functions which merge such trusts, and define a class of functions for which merging may be done locally while maintaining these properties. We give examples of specific functions and apply them to data from Epinions and our BibServ bibliography server. Experiments confirm that the methods are robust to noise, and do not put unreasonable expectations on users. We hope that these methods will help move the Semantic Web closer to fulfilling its promise.

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Section: Modelmentioning

confidence: 99%

“…Any user may assert her personal belief in the statement, which is taken from [0,1]. A high value means that the statement is accurate, credible, and/or relevant.…”

Section: Modelmentioning

confidence: 99%

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Trust Management for the Semantic Web

Richardson

Agrawal

Domingos

2003

Lecture Notes in Computer Science

681

379

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“…In addition, using trust levels, it avoids the non-trusted nodes in the routes that may use brute force attacks and may decrypt messages if enough parts of the message are available to them. This technique uses a variation of the trust models used in [17] and [18]. A node is assigned a discrete trust level in the range of to 4.…”

Section: Message Security Using Trust-based Multipath Routing : Tmrmentioning

confidence: 99%

Security Establishment in MANET Systems using FACES

Manivannan¹,

J.Shakila²

2013

IJSEA

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Friend based Ad-hoc routing using Challenges to Establish Security (FACES) is an algorithm to provide secure routing in ad-hoc mobile networks. The scheme proposed has been drawn from a network of friends in real life scenarios. The algorithm is divided into four stages, viz. Challenge your neighbour, Rate Friends, Share Friends and Route through Friends. One of the major advantages of this scheme is that the nodes do not need to promiscuously listen to the traffic passing through their neighbours. The information about the malicious nodes is gathered effectively by using Challenges, which reduces the overhead on networks. As a result of this scheme of operation, the network is able to effectively isolate the malicious nodes which are left with no role to play in the ad-hoc network. One major benefit of this scheme is that the nodes do not need to promiscuously listen to the traffic passing through their neighbours. The information about the malicious nodes is gathered effectively by using Challenges. This reduces the overhead on the network significantly. Through extensive simulation analysis it was inferred that this scheme provides an efficient approach towards security and easier detection of malicious nodes in the mobile ad-hoc network.

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“…However, in large-scale environments, the amount of available direct experience is often insufficient or even non-existent. In such circumstances, prediction is often based on trustor's "indirect experience" -opinions obtained from other agents that determine the target agent's reputation [17,45,1]. Simple aggregations, like a seller's ranking on eBay, rely on access to global information, like the history of the agent's behavior.…”

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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A distributed trust model

Cited by 495 publications

References 6 publications

Trust Management for the Semantic Web

Trust Management for the Semantic Web

Security Establishment in MANET Systems using FACES

Trust beyond reputation: A computational trust model based on stereotypes

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