In the Internet of Things (IoT), a huge amount of valuable data is generated by various IoT applications. As the IoT technologies become more complex, the attack methods are more diversified and can cause serious damages. Thus, establishing a secure IoT network based on user trust evaluation to defend against security threats and ensure the reliability of data source of collected data have become urgent issues, in this paper, a Data Fusion and transfer learning empowered granular Trust Evaluation mechanism (DFTE) is proposed to address the above challenges. Specifically, to meet the granularity demands of trust evaluation, time–space empowered fine/coarse grained trust evaluation models are built utilizing deep transfer learning algorithms based on data fusion. Moreover, to prevent privacy leakage and task sabotage, a dynamic reward and punishment mechanism is developed to encourage honest users by dynamically adjusting the scale of reward or punishment and accurately evaluating users’ trusts. The extensive experiments show that: (i) the proposed DFTE achieves high accuracy of trust evaluation under different granular demands through efficient data fusion; (ii) DFTE performs excellently in participation rate and data reliability.

在物联网 (IoT) 中，各种物联网应用会产生大量有价值的数据。随着物联网技术变得越来越复杂，攻击方式也越来越多样化，可能造成严重的破坏。因此，建立基于用户信任评估的安全物联网网络以防御安全威胁并确保采集数据的数据来源的可靠性已成为紧迫的问题，本文提出了一种数据融合和迁移学习赋能的粒度信任评估机制（DFTE）建议解决上述挑战。具体而言，为了满足信任评估的粒度需求，利用基于数据融合的深度迁移学习算法构建时空赋能的细粒度/粗粒度信任评估模型。此外，为了防止隐私泄露和任务破坏，建立动态奖惩机制，通过动态调整奖惩规模，准确评估用户信任度，鼓励诚实用户。大量实验表明：（i）所提出的DFTE通过有效的数据融合在不同粒度需求下实现了信任评估的高精度；(ii) DFTE 在参与率和数据可靠性方面表现出色。