Privacy-Preserving and Traceable Federated Learning for data sharing in industrial IoT applications

Author:

Chen JunbaoORCID,Xue JingfengORCID,Wang YongORCID,Huang LuORCID,Baker TharORCID,Zhou ZhixiongORCID

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference25 articles.

1. A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion;Ali;Information Fusion,2020

2. Privacy-preserving deep learning via additively homomorphic encryption;Aono;IEEE Transactions on Information Forensics and Security,2017

3. Poster: A reliable and accountable privacy-preserving federated learning framework using the blockchain;Awan,2019

4. How to backdoor federated learning;Bagdasaryan,2020

5. Practical secure aggregation for privacy-preserving machine learning;Bonawitz,2017

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1. Secure Federated Learning With Fully Homomorphic Encryption for IoT Communications;IEEE Internet of Things Journal;2024-02-01

2. Towards Privacy-Aware Federated Learning for User-Sensitive Data;2023 Fifth International Conference on Blockchain Computing and Applications (BCCA);2023-10-24

3. Towards trustworthy federated learning: a blockchain-based architecture for auditing, traceability, and verification;Fourth International Conference on Computer Science and Communication Technology (ICCSCT 2023);2023-10-11

4. Attribute-Centric and Synthetic Data Based Privacy Preserving Methods: A Systematic Review;Journal of Cybersecurity and Privacy;2023-09-11

5. Comprehensive Survey on AI-Based Technologies for Enhancing IoT Privacy and Security: Trends, Challenges, and Solutions;HUM-CENT COMPUT INFO;2023

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