A priori trust inference with context-aware stereotypical deep learning

Zhou, Peng; Gu, Xiaojing; Zhang, Jie; Fei, Minrui

doi:10.1016/j.knosys.2015.08.003

Cited by 28 publications

(13 citation statements)

References 30 publications

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“…In particular, privacy protections on trust evidence are not explored in all methods. Only a few of the existing methods can resist attacks that occur in trust evaluation [D'Angelo et al 2017;Zhou et al 2015;Tran 2015, 2017]. Among the existing methods, we roughly divide those with contextual awareness into two categories.…”

Section: Discussionmentioning

confidence: 99%

“…Methods that consider computational overhead are also rare. Most works do not consider this quality attribute, which was only considered in Zhou et al [2015], Yahyaoui and Zhioua [2013], Yahyaoui and Al-Mutairi [2016], Huang et al [2005], and Kim and Song [2011].…”

Section: Discussionmentioning

confidence: 99%

“…3.2.1.3 Models with Continuous Numeral Ratings. Zhou et al [2015] designed a Context-Awareness Stereotypical Trust deep learning framework (CAST) for inferring a priori trust value in an evidence-sparse context by learning seven context-awareness stereotypes in evidence-dense contexts. They trained an inference model for each of seven stereotypes by using a deep model and employed evidences with trust values for model training.…”

Section: Models With Discrete Numeralmentioning

confidence: 99%

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A Survey on Trust Evaluation Based on Machine Learning

et al. 2020

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Trust evaluation is the process of quantifying trust with attributes that influence trust. It faces a number of severe issues such as lack of essential evaluation data, demand of big data process, request of simple trust relationship expression, and expectation of automation. In order to overcome these problems and intelligently and automatically evaluate trust, machine learning has been applied into trust evaluation. Researchers have proposed many methods to use machine learning for trust evaluation. However, the literature still lacks a comprehensive literature review on this topic. In this article, we perform a thorough survey on trust evaluation based on machine learning. First, we cover essential prerequisites of trust evaluation and machine learning. Then, we justify a number of requirements that a sound trust evaluation method should satisfy, and propose them as evaluation criteria to assess the performance of trust evaluation methods. Furthermore, we systematically organize existing methods according to application scenarios and provide a comprehensive literature review on trust evaluation from the perspective of machine learning's function in trust evaluation and evaluation granularity. Finally, according to the completed review and evaluation, we explore some open research problems and suggest the directions that are worth our research effort in the future.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Models With Discrete Numeralmentioning

confidence: 99%

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A Survey on Trust Evaluation Based on Machine Learning

et al. 2020

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“…A separate thread of research relies solely on stereotypical intrinsic properties of the agents and the environment in which they operate, to derive a likelihood of trustworthiness without using any evidence. These approaches (Zhou et al 2015;Noorian, Marsh, and Fleming 2011;Singh 2011;Liu and Datta 2012) are considered a complement to evidence-based trust and are beneficial when there is no enough evidence available.…”

Section: Related Workmentioning

confidence: 99%

COBRA: Context-Aware Bernoulli Neural Networks for Reputation Assessment

Zeynalvand

Luo²,

Zhang

2020

AAAI

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Trust and reputation management (TRM) plays an increasingly important role in large-scale online environments such as multi-agent systems (MAS) and the Internet of Things (IoT). One main objective of TRM is to achieve accurate trust assessment of entities such as agents or IoT service providers. However, this encounters an accuracy-privacy dilemma as we identify in this paper, and we propose a framework called Context-aware Bernoulli Neural Network based

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“…In general, HTTPAS performs active authentication based on trust management theory. However, since the trust management is a big research topic, we should narrow down our scope and exclude the trust management works that are somewhat far away from this paper (such as the social trust management [14–16], the behaviour‐based trust modelling [17, 18] and the stereotypical trust management methods [19]). As a result, we only focus on the trust management methods built on the CA model for authentication.…”

Section: Related Workmentioning

confidence: 99%