Securing Health Data on the Blockchain: A Differential Privacy and Federated Learning Framework

TL;DR

This paper presents a novel framework combining differential privacy, federated learning, and blockchain technology to secure health data in IoT-based healthcare systems, balancing privacy and data utility.

Contribution

It introduces an integrated privacy-preserving framework that leverages dynamic personalization, adaptive noise, and blockchain for secure health data analytics in IoT environments.

Findings

Achieves strong privacy guarantees against attacks.

Maintains high accuracy in health analytics tasks.

Demonstrates practical blockchain transaction latency.

Abstract

This study proposes a framework to enhance privacy in Blockchain-based Internet of Things (BIoT) systems used in the healthcare sector. The framework addresses the challenge of leveraging health data for analytics while protecting patient privacy. To achieve this, the study integrates Differential Privacy (DP) with Federated Learning (FL) to protect sensitive health data collected by IoT nodes. The proposed framework utilizes dynamic personalization and adaptive noise distribution strategies to balance privacy and data utility. Additionally, blockchain technology ensures secure and transparent aggregation and storage of model updates. Experimental results on the SVHN dataset demonstrate that the proposed framework achieves strong privacy guarantees against various attack scenarios while maintaining high accuracy in health analytics tasks. For 15 rounds of federated learning with an epsilon value of 8.0, the model obtains an accuracy of 64.50%. The blockchain integration, utilizing Ethereum, Ganache, Web3.py, and IPFS, exhibits an average transaction latency of around 6 seconds and consistent gas consumption across rounds, validating the practicality and feasibility of the proposed approach.