Title
A Redactable Blockchain Framework for Secure Federated Learning in Industrial Internet of Things
Abstract
Industrial Internet of Things (IIoT) facilitate private data collecting via (a broad range of) sensors, and the analysis of such data can inform decision making at different levels. Federated learning (FL) can be used to analyze the collected data, in privacy-preserving manner by transmitting model updates instead of private data in IIoT networks. The FL framework is, however, vulnerable because model updates are easily tampered with by malicious agents. Motivated by this observation, we propose a novel chameleon hash scheme with a changeable trapdoor (CHCT) for secure FL in IIoT settings. Our scheme imposes various constraints on the use of trapdoor. We give a rigorous security analysis on our CHCT scheme. We also instantiate the CHCT scheme as a redactable medical blockchain (RMB). The experimental evaluations demonstrate the practical utility of CHCT in terms of accuracy and efficiency.
Year
DOI
Venue
2022
10.1109/JIOT.2022.3162499
IEEE Internet of Things Journal
Keywords
DocType
Volume
Blockchain,chameleon hash,federated learning (FL),Industrial Internet of Things (IIoT)
Journal
9
Issue
Citations 
PageRank 
18
0
0.34
References 
Authors
26
7
Name
Order
Citations
PageRank
Jiannan Wei100.34
Qinchuan Zhu200.34
Li Qian-Mu33314.78
Laisen Nie400.34
Zhangyi Shen500.34
Kim-Kwang Raymond Choo64103362.49
Keping Yu712424.51