Privacy-Preserving Healthcare Data in IoT: A Synergistic Approach with Deep Learning and Blockchain

TL;DR

This paper presents a comprehensive security framework for IoT healthcare systems that combines trust assessment, blockchain, and deep learning to enhance data security, integrity, and real-time threat detection.

Contribution

It introduces a novel three-phase security framework integrating trust estimation, blockchain, and LSTM-based anomaly detection specifically for IoT healthcare environments.

Findings

Achieves a 2% increase in precision, accuracy, and recall.

Attains a 5% higher attack detection rate.

Reduces false alarm rate by 3%.

Abstract

The integration of Internet of Things (IoT) devices in healthcare has revolutionized patient care by enabling real-time monitoring, personalized treatments, and efficient data management. However, this technological advancement introduces significant security risks, particularly concerning the confidentiality, integrity, and availability of sensitive medical data. Traditional security measures are often insufficient to address the unique challenges posed by IoT environments, such as heterogeneity, resource constraints, and the need for real-time processing. To tackle these challenges, we propose a comprehensive three-phase security framework designed to enhance the security and reliability of IoT-enabled healthcare systems. In the first phase, the framework assesses the reliability of IoT devices using a reputation-based trust estimation mechanism, which combines device behavior analytics with off-chain data storage to ensure scalability. The second phase integrates blockchain technology with a lightweight proof-of-work mechanism, ensuring data immutability, secure communication, and resistance to unauthorized access. The third phase employs a lightweight Long Short-Term Memory (LSTM) model for anomaly detection and classification, enabling real-time identification of cyber threats. Simulation results demonstrate that the proposed framework outperforms existing methods, achieving a 2% increase in precision, accuracy, and recall, a 5% higher attack detection rate, and a 3% reduction in false alarm rate. These improvements highlight the framework's ability to address critical security concerns while maintaining scalability and real-time performance.