The Eigentrust algorithm for reputation management in P2P networks

Kamvar, Sepandar; Schlösser, Mario; García-Molina, Héctor

doi:10.1145/775240.775242

Cited by 1,576 publications

(1,192 citation statements)

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“…However, this can converge easier to a unique failure point. In [18] the information is distributed throughout the pairs, which store it. Another approach works with storage rate [19], when each pair stores information about other pairs trustworthiness.…”

Section: B Trustworthiness Managementmentioning

confidence: 99%

Trustworthiness Management Through Social Relationships in Internet of Medical Things

Brittes

Schneider

Wille

2017

JCIS

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Abstract-In recent years, the Internet of Things (IoT) paradigm has been introduced, getting attention as an emerging technology, built upon pervasive connectivity of objects in heterogeneous networks. In this context, biomedical networks are supporting biomedical environments, with many biomedical devices named objects, that are able to interact with each other and collaborate in order to achieve a common objective (diagnosis, treatment, and monitoring, patient rehabilitation). For this scenario, we use the concept of Internet of Medical Things (IoMT), as well the challenges to develop trustworthiness networks to exchange data in a biomedical environment. We propose a model to manage trustworthiness for IoMT networks based on social network concepts and priority criteria. Our model is based on a trust recommendation index, computed using the proposed trustworthiness management protocol. We analyze the effect of our protocol and demonstrate its effectiveness simulating an IoMT scenario, with improvements when analyzing the relationships establishment and network performance, also considering data and services exchanges.

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Section: B Trustworthiness Managementmentioning

confidence: 99%

Trustworthiness Management Through Social Relationships in Internet of Medical Things

Brittes

Schneider

Wille

2017

JCIS

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“…Additionally, a series of algorithms have been proposed through extending PageRank for signed networks. For example, EigenTrust [20] first removes the negative links from the network and then, ranks the nodes in the resulting network, so, a negative link between two nodes, is treated just like an absent link, while they represent totally different facts. PolarityRank [21] is another algorithm that classifies the nodes' labels through propagation of ranks from some predetermined representatives for both positive and negative groups.…”

Section: Personalized Ranking In Signed Networkmentioning

confidence: 99%

SibRank: Signed bipartite network analysis for neighbor-based collaborative ranking

Shams

Haratizadeh

2016

Physica A: Statistical Mechanics and its Applications

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Collaborative ranking is an emerging field of recommender systems that utilizes users' preference data rather than rating values. Unfortunately, neighbor-based collaborative ranking has gained little attention despite its more flexibility and justifiability. This paper proposes a novel framework, called SibRank that seeks to improve the state of the art neighbor-based collaborative ranking methods. SibRank represents users' preferences as a signed bipartite network, and finds similar users, through a novel personalized ranking algorithm in signed networks.

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“…Finally, the trustworthiness of a page p is, roughly speaking, the probability that the surfer will be at p after a large number of steps. An important version of PageRank adapted to peer-to-peer systems can be found in [13].…”

Section: Evaluating Trustworthiness: Different Research Trendsmentioning

confidence: 99%

Evaluating trustworthiness from past performances: interval-based approaches

Ben-Naim

Prade

2012

Ann Math Artif Intell

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In many multi-agent systems, especially in the field of e-commerce, the users have to decide whether they sufficiently trust an agent to achieve a certain goal. To help users to make such decisions, an increasing number of trust systems have been developed. By trust system, we mean a system that gathers information about an agent and evaluates its trustworthiness on the basis of this information. The aim of the present paper is to develop, and analyze from an axiomatic point of view, new trust systems based on intervals. More precisely, we assume that a set of grades describing the past performances of an agent is given. Then, the goal is to construct an interval that summarizes these grades. In our opinion, such an interval gives a good account of the trustworthiness of the agent. In addition, this kind of representation format overcomes certain limitations (at a certain cost) of the approaches that represent trustworthiness by a single number. We establish seven axioms that should be satisfied by a summarizing method. Next, we develop two new methods. The first one is based on the idea that certain concentrations of grades are strong enough to pull the bounds of the summarizing interval towards themselves. The second one represents data in the setting of possibility theory, and then computes lower and upper expected values. Finally, we check that our methods satisfy the axioms introduced before, which provide theoretical justifications for them.

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The Eigentrust algorithm for reputation management in P2P networks

Cited by 1,576 publications

References 0 publications

Trustworthiness Management Through Social Relationships in Internet of Medical Things

Trustworthiness Management Through Social Relationships in Internet of Medical Things

SibRank: Signed bipartite network analysis for neighbor-based collaborative ranking

Evaluating trustworthiness from past performances: interval-based approaches

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