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 Reputation Assessment (COBRA) to address this challenge. COBRA encapsulates agent interactions or transactions, which are prone to privacy leak, in machine learning models, and aggregates multiple such models using a Bernoulli neural network to predict a trust score for an agent. COBRA preserves agent privacy and retains interaction contexts via the machine learning models, and achieves more accurate trust prediction than a fully-connected neural network alternative. COBRA is also robust to security attacks by agents who inject fake machine learning models; notably, it is resistant to the 51-percent attack. The performance of COBRA is validated by our experiments using a real dataset, and by our simulations, where we also show that COBRA outperforms other state-of-the-art TRM systems.

中文翻译：

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COBRA：用于声誉评估的上下文感知伯努利神经网络

信任和声誉管理 (TRM) 在多代理系统 (MAS) 和物联网 (IoT) 等大规模在线环境中发挥着越来越重要的作用。TRM 的一个主要目标是实现对代理或物联网服务提供商等实体的准确信任评估。然而，这遇到了我们在本文中确定的准确性 - 隐私困境，我们提出了一个称为基于上下文感知伯努利神经网络的声誉评估（COBRA）的框架来解决这一挑战。COBRA 将容易发生隐私泄露的代理交互或交易封装在机器学习模型中，并使用伯努利神经网络聚合多个此类模型来预测代理的信任分数。COBRA 通过机器学习模型保护代理隐私并保留交互上下文，并实现比完全连接的神经网络替代方案更准确的信任预测。COBRA 还可以抵御注入虚假机器学习模型的代理的安全攻击；值得注意的是，它可以抵抗 51% 的攻击。COBRA 的性能通过我们使用真实数据集的实验和我们的模拟得到验证，我们还表明 COBRA 优于其他最先进的 TRM 系统。