An iterative algorithm for trust and reputation management
An iterative algorithm for trust and reputation management
复制标题
信任和声誉管理的迭代算法
DOI：
10.1109/isit.2009.5205441
复制
发表时间：
2009
期刊：
影响因子：
--
通讯作者：
F. Fekri
中科院分区：
文献类型：
--
作者：
Erman Ayday;Hanseung Lee;F. Fekri
信任和声誉在大多数环境中扮演着关键的角色，其中实体参与彼此之间的各种交易和协议。服务的接受者别无选择，只能依赖服务提供者基于其先前业绩的声誉。本文介绍了一种迭代的信任和声誉管理方法，简称ITRM。所提出的算法可以应用于集中式方案，其中中央权威机构收集报告并形成服务提供商的声誉以及（服务）消费者的报告/评级可信度。所提出的迭代算法的启发低密度奇偶校验码的迭代译码的二分图。该计划是强大的过滤掉同行谁提供不可靠的评级。我们提供了一个详细的评估ITRM通过分析和计算机模拟。此外，ITRM与一些众所周知的声誉管理技术（例如，平均方案，贝叶斯方法和聚类过滤）表明我们的方案在抵抗攻击（例如，填选票、说坏话）和效率。此外，我们表明，建议的ITRM的计算复杂度是远远低于集群过滤，它具有最接近的性能（ITRM）在弹性攻击。具体来说，ITRM的复杂度是线性的客户端的数量，而集群过滤是二次的。
Trust and reputation play critical roles in most environments wherein entities participate in various transactions and protocols among each other. The recipient of the service has no choice but to rely on the reputation of the service provider based on the latter's prior performance. This paper introduces an iterative method for trust and reputation management referred as ITRM. The proposed algorithm can be applied to centralized schemes, in which a central authority collects the reports and forms the reputations of the service providers as well as report/rating trustworthiness of the (service) consumers. The proposed iterative algorithm is inspired by the iterative decoding of low-density parity-check codes over bipartite graphs. The scheme is robust in filtering out the peers who provide unreliable ratings. We provide a detailed evaluation of ITRM via analysis and computer simulations. Further, comparison of ITRM with some well-known reputation management techniques (e.g., Averaging Scheme, Bayesian Approach and Cluster Filtering) indicates the superiority of our scheme both in terms of robustness against attacks (e.g., ballot-stuffing, bad-mouthing) and efficiency. Furthermore, we show that the computational complexity of the proposed ITRM is far less than the Cluster Filtering; which has the closest performance (to ITRM) in terms of resiliency to attacks. Specifically, the complexity of ITRM is linear in the number of clients, while that of the Cluster Filtering is quadratic.
影响因子：
2.5
作者：
Richardson, TJ;Urbanke, RL
通讯作者：
Urbanke, RL
n 二进制擦除通道上LDPC码的自适应译码算法
DOI：
--
发表时间：
2007
期刊：
影响因子：
--
作者：
Gou Hosoya;Hideki Yagi;Toshiyasu Matsushima;Shigeichi Hirasawa
通讯作者：
Shigeichi Hirasawa