Blockchain-Enabled Federated Learning Data Protection Aggregation Scheme With Differential Privacy and Homomorphic Encryption in IIoT

With rapid growth in data volume generated from different industrial devices in IoT, the protection for sensitive and private data in data sharing has become crucial. At present, federated learning for data security has arisen, and it can solve the security concerns on data sharing by model sharing on Internet of mutual distrust. However, the hackers still launch attack aiming at the security vulnerabilities (e.g., model extraction attack and model reverse attack) in federated learning. In this article, to address the above problems, we first design an application model of blockchain-enabled federated learning in Industrial Internet of Things (IIoT), and formulate our data protection aggregation scheme based on the above model. Then, we give the distributed K-means clustering based on differential privacy and homomorphic encryption, and the distributed random forest with differential privacy and the distributed AdaBoost with homomorphic encryption methods, which enable multiple data protection in data sharing and model sharing. Finally, we integrate the methods with blockchain and federated learning, and provide the complete security analysis. Extensive experimental results show that our aggregation scheme and working mechanism have the better performance in the selected indicators.

I. Introduction

With the boom of modern Information and Communications Technology (ICT), the fourth industrial revolution (Industry 4.0) has overturned the traditional industrial development model. Industrial Internet of Things (IIoT), where the huge amounts of data are generated by the connected industrial devices every day, is considered as one of the key technologies for the implementation of Industry 4.0. The characteristics of traditional industrial data were small quantity, simple construction, and unidirectional transmission, while, the features of data in IIoT are massive data, complicated structure, and two-way transmission. Business application and direction and path of data flow are more complicated, and data types and protection demand are diverse. It should not be surprising that the safety and security requirements in IIoT are generally stricter than those found in a typical IoT scenario [1]. Therefore, how to protect and use these valuable data in IIoT to share in efficient, secure, and economic ways becomes a burning issue for owners and providers. In recent years, we have seen an unprecedented growth of demand in physical data and logical data security of IIoT.