【Author】
Xu, Jianlong; Lin, Jian; Liang, Wei; Li, Kuan-Ching
【Source】CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
【Abstract】The integration of blockchain and the Internet of Things (IoT) is seen as having significant potential. In IoT Environments, Blockchain builds a trusted environment for IoT information sharing, where information is immutable and reliable. In particular, when edge devices are connected to a blockchain network, they need to be connected to reliable blockchain peers for synchronizing with valid data. Therefore, blockchain reliability prediction has gained attention owing to its ability to help users find highly reliable blockchain peers. Contextual information has been considered useful in many studies for generating highly personalized blockchain reliability predictions. However, these contextual factors are privacy-sensitive, and therefore disclosing them to third parties is risky. To address this challenge, we propose a privacy-preserving personalized blockchain reliability prediction model through federated learning neural collaborative filtering (FNCF) in IoT. Our model allows users to achieve user privacy protection without passing data to a third party and provides personalized predictions for users. We can also leverage the power of edge computing to enable a fast data processing capability and low latency required by IoT applications. Finally, our model was evaluated using a set of experiments based on real-world datasets. The experimental results show that the proposed model achieves high accuracy, efficiency, and scalability.
【Keywords】Privacy preservation; Federated learning; Blockchain; Reliability prediction; Internet of things; Edge computing
【标题】通过物联网环境中的联邦学习进行隐私保护的个性化区块链可靠性预测
【摘要】区块链与物联网 (IoT) 的整合被视为具有巨大潜力。在物联网环境中，区块链为物联网信息共享构建了一个可信的环境，其中信息是不可变的和可靠的。特别是，当边缘设备连接到区块链网络时，它们需要连接到可靠的区块链对等点以与有效数据同步。因此，区块链可靠性预测因其能够帮助用户找到高度可靠的区块链对等点而受到关注。在许多研究中，上下文信息被认为对生成高度个性化的区块链可靠性预测很有用。但是，这些上下文因素是隐私敏感的，因此将它们披露给第三方是有风险的。为了应对这一挑战，我们通过物联网中的联邦学习神经协同过滤（FNCF）提出了一种隐私保护的个性化区块链可靠性预测模型。我们的模型允许用户在不将数据传递给第三方的情况下实现用户隐私保护，并为用户提供个性化的预测。我们还可以利用边缘计算的力量来实现物联网应用所需的快速数据处理能力和低延迟。最后，我们使用一组基于真实数据集的实验来评估我们的模型。实验结果表明，该模型具有较高的准确性、效率和可扩展性。
【关键词】隐私保护；联邦学习；区块链；可靠性预测；物联网;边缘计算
【文献类型】Article; Early Access
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