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A Trust Model with Statistical Foundation

机译:统计基础的信任模型

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摘要

The widespread use of the Internet signals the need for a better understanding of trust as a basis for secure on-line interaction. In the face of increasing uncertainty and risk, users and machines must be allowed to reason effectively about the trustworthiness of other entities. In this paper, we propose a trust model that assists users and machines with decision-making in online interactions by using past behavior as a predictor of likely future behavior. We develop a general method to automatically compute trust based on self-experience and the recommendations of others. Furthermore, we apply our trust model to several utility models to increase the accuracy of decision-making in different contexts of Web Services.
机译:Internet的广泛使用表明需要更好地了解信任,将其作为安全在线交互的基础。面对不断增加的不确定性和风险,必须让用户和机器有效地推理其他实体的可信度。在本文中,我们提出了一种信任模型,该模型通过使用过去的行为作为可能的未来行为的预测因素来协助用户和机器进行在线交互中的决策。我们开发了一种基于自身经验和他人建议自动计算信任度的通用方法。此外,我们将信任模型应用于多个实用程序模型,以提高在Web服务的不同上下文中决策的准确性。

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