The Beta Reputation System

By Roslan Ismail, Audun Jøsang. Bled eConference, 2002

1,849 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b451a8a074f61fe7f019dc7f8c3262636d4a7b7d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Foliar heavy metal uptake, toxicity and detoxification in plants: A comparison of foliar and root metal uptake. (2017, unknown): “While, the implicit trust refers to the trust inferred from some evidence such as feature similarity among users [50] [5], their behaviors [24] [31], the age of their relationships [18] [27].”
Trustworthiness Management in the Social Internet of Things (2014, methodology): “Finally, some algorithms make use of probabilistic estimation techniques [28], [29] and the maximum likelihood estimation [29] to match the reputation value into the probability that a peer will cooperate.”
Trust network analysis with subjective logic (2006, methodology): “The trust representation of subjective logic is directly compatible with the reputation representation of Bayesian reputation systems [ 12 , 13, 25, 24].”
Information theoretic framework of trust modeling and evaluation for ad hoc networks (2006, methodology): “In [25]–[27], a Bayesian model is used to take binary ratings as input and compute reputation scores by statistically updating beta probability density functions.”
TRAVOS: Trust and Reputation in the Context of Inaccurate Information Sources (2006, methodology): “[11, 8, 18, 10]).”
A survey of trust in computer science and the Semantic Web (2007, methodology): “…(Damiani et al., 2002) [50] (Olmedilla et al., 2005) [7] Application-specific reputation (Pirzada and McDonald, 2004) [61] (Dash et al., 2004) [62] (Josang and Ismail, 2002) [63] ) [65] (Acrement, 2002) [66] (Mui et al., 2002) [5] (Staab et al., 2004) [67] Computational and online trust models…”
Recruitment Framework for Participatory Sensing Data Collections (2010, methodology): “Particularly, these systems rely on ratings, either positive or negative, and use probability distributions, such as the Beta distribution, to come up with reputation scores [31].”
A Robust Reputation System for P2P and Mobile Ad-hoc Networks (2004, background): “Josang and Ismail [ 13 ] also use a Bayesian approach, it is, however, centralized and discounts the belief in second-hand information according to the reputation of agents, equating the trustworthiness of an agent as a witness with its performance in the base system.”
Trust Computations and Trust Dynamics in Mobile Adhoc Networks: A Survey (2012, unknown): “In [54] Beta distribution is used.”
Uncheatable Reputation for Distributed Computation Markets (2006, methodology): “The promise of such reputation frameworks [3, 5, 6, 11, 12, 13, 20] is to offer a low cost, scalable method for assessing reliability (and possibly level of trust) of connected system entities.”
Filtering Out Unfair Ratings in Bayesian Reputation Systems (2004, methodology): “We use the reputation system described in Jøsang & Ismail (2002) [9], and integrate the filtering method to the reputation systems simulator described in Jøsang et al. (2003) [8].”
Reputation-based framework for high integrity sensor networks (2004, methodology): “We adopt a classical betabinomial framework for estimating reputations [Gelman et al. 2003; Josang and Ismail 2002].”
PeerTrust: supporting reputation-based trust for peer-to-peer electronic communities (2004, background): “Josang and Ismail [16] and Josang and Tran [17] developed and evaluated the beta reputation system for electronic markets based on distribution by modeling reputation as posterior probability given a sequence of experiences.”
Trust Management for SOA-Based IoT and Its Application to Service Composition (2016, methodology): “We adopt Bayesian framework [14] as the underlying model for evaluating direct trust from direct user satisfaction experiences.”
A survey of trust and reputation systems for online service provision (2007, background): “Past ratings can be discounted by a forgetting factor [31], aging factor [7] or fading factor [6].”