Enhancing data security in massive data sets using blockchain and federated learning: a loosely coupled approach

作     者：Kang, Haiyan Wu, Bing 

作者机构：Beijing Informat Sci & Technol Univ Dept Informat Secur Beijing Peoples R China 

出 版 物：《INTERNATIONAL JOURNAL OF INTERNET PROTOCOL TECHNOLOGY》 (Int. J. Internet Protoc. Technol.)

年 卷 期：2024年第17卷第1期

页      面：31-41页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Beijing Advanced Innovation Centre for Future Blockchain and Privacy Computing Fund [GJJ-23] National Social Science Foundation, China [21BTQ079] 

主　　题：federated learning blockchain differential privacy massive data processing 

摘      要：The properties of the blockchain are not suitable for the storage of sensitive privacy data, and the excellent characteristics of the blockchain will be seriously affected by the existence of massive amounts of data on the chain. To address the above issues, propose a Loosely Coupled Local Differential Privacy Blockchain Federated Learning method (LL-BCFL). First of all, a client selection mechanism is put forward to ensure honesty and positivity in joining the training client and the correct effectiveness of the final global model aggregation. Secondly, use federated learning to alleviate the data silos phenomenon and achieve joint training of big data stored in distributed multi-parties. Additionally, a differential privacy method is introduced to act on federated learning networks to avoid inference attacks. Finally, the MNIST dataset was used to confirm the availability of the LL-BCFL method on balanced and unbalanced datasets.