Privacy Preservation in AI-Driven IoT for Vehicles via Hierarchical Sharding Blockchain
Mingzhe Zhai, Qianhong Wu, Yizhong Liu, Bo Qin, Yang Yang, Ghulam Muhammad, Prayag Tiwari · IEEE Internet of Things Journal · 2024
The AI-driven Internet of Things (AIoT) has been widely applied in the field of Internet of Vehicles (IoV) for vehicular cooperation. Federated learning (FL), due to its ability to protect users’ data privacy, reduce communication overhead, and facilitate real-time decision making, is widely applied in the augmented intelligence of things for vehicles (AIoV). However, integrating FL with AIoV poses challenges, including the absence of fine-grained access control, insufficient safeguards for FL tasks and vehicle identities, inadequate security for data transmission, and shortcomings in protecting data storage. These vulnerabilities may lead to risks such as vehicle tracking, model information theft, and data tampering. To address these challenges, we propose a privacy preservation mechanism for AIoV via cloud–edge–vehicle hierarchical sharding blockchain. First, we propose a hierarchical anonymous authentication scheme for IoV devices with stronger scalability and higher fault tolerance. Vehicles only know the attributes of each other or which shard they belong to. Second, we present a secure FL task assignment scheme for AIoV. Edge nodes utilize attribute-based encryption to deploy fine-grained FL tasks based on vehicle attributes. Only users who meet the attributes can decrypt the content, protecting FL tasks content and participant identities. Third, we present a secure data transmission scheme between AIoV devices to protect the identity and data privacy of both parties, while also achieving noninteractive key agreement. Additionally, we propose a scalable secure data sharing and storage scheme based on hierarchical sharding blockchain, aiming to reduce storage overhead and minimize trust costs.