FedBlockHealth: A Synergistic Approach to Privacy and Security in IoT-Enabled Healthcare through Federated Learning and Blockchain

TL;DR

This paper introduces a hybrid federated learning and blockchain framework to enhance privacy, security, and efficiency in IoT healthcare applications, addressing key challenges in data protection and integrity.

Contribution

It presents a novel combination of federated learning and blockchain with cryptographic security tailored for resource-constrained IoT healthcare devices.

Findings

Effective privacy preservation demonstrated on EMNIST datasets

Blockchain ensures data integrity and access control

Framework maintains computational efficiency in IoT environments

Abstract

The rapid adoption of Internet of Things (IoT) devices in healthcare has introduced new challenges in preserving data privacy, security and patient safety. Traditional approaches need to ensure security and privacy while maintaining computational efficiency, particularly for resource-constrained IoT devices. This paper proposes a novel hybrid approach combining federated learning and blockchain technology to provide a secure and privacy-preserved solution for IoT-enabled healthcare applications. Our approach leverages a public-key cryptosystem that provides semantic security for local model updates, while blockchain technology ensures the integrity of these updates and enforces access control and accountability. The federated learning process enables a secure model aggregation without sharing sensitive patient data. We implement and evaluate our proposed framework using EMNIST datasets, demonstrating its effectiveness in preserving data privacy and security while maintaining computational efficiency. The results suggest that our hybrid approach can significantly enhance the development of secure and privacy-preserved IoT-enabled healthcare applications, offering a promising direction for future research in this field.