Bayesian Network Based Trust Management
Bayesian Network Based Trust Management
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DOI：
10.1007/11839569_24
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发表时间：
2006-09
期刊：
影响因子：
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通讯作者：
Yong Wang;V. Cahill;Elizabeth Gray;C. Harris;L. Liao
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文献类型：
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作者：
Yong Wang;V. Cahill;Elizabeth Gray;C. Harris;L. Liao
信任是不确定环境中安全协作的重要组成部分。信任管理可用于推理实体之间未来的交互。在基于声誉的信任管理中，实体的声誉通常建立在与该实体有直接互动的人的评级之上。在本文中，我们提出了一种基于贝叶斯网络的信任管理模型。为了推断实体行为不同方面的信任，我们使用多维应用程序特定信任值，并使用单个贝叶斯网络评估每个维度。这使得扩展模型以涉及更多信任维度以及结合贝叶斯网络以形成有关实体整体可信度的意见变得容易。每个实体可以根据自己的标准评估其同行。标准和同伴行为的动态特征可以通过更新贝叶斯网络来捕获。风险与信任明确结合，帮助用户做出决策。在本文中，我们证明我们的系统可以做出准确的信任推断，并且对不公平的评估者具有鲁棒性。
Trust is an essential component for secure collaboration in uncertain environments. Trust management can be used to reason about future interactions between entities. In reputation-based trust management, an entity’s reputation is usually built on ratings from those who have had direct interactions with the entity. In this paper, we propose a Bayesian network based trust management model. In order to infer trust in different aspects of an entity’s behavior, we use multi-dimensional application specific trust values and each dimension is evaluated using a single Bayesian network. This makes it easy both to extend the model to involve more dimensions of trust and to combine Bayesian networks to form an opinion about the overall trustworthiness of an entity. Each entity can evaluate his peers according to his own criteria. The dynamic characteristics of criteria and of peer behavior can be captured by updating Bayesian networks. Risk is explicitly combined with trust to help users making decisions. In this paper, we show that our system can make accurate trust inferences and is robust against unfair raters.