Blockchain Powered Edge Intelligence for U-Healthcare in Privacy Critical and Time Sensitive Environment

2025-05-31

AI Summary

Existing blockchain-empowered edge intelligence systems for privacy-sensitive, latency-critical ubiquitous healthcare (U-Healthcare) suffer from security vulnerabilities, privacy leakage risks, and real-time performance bottlenecks between edge gateways and distributed storage. Method: This paper proposes a blockchain-edge collaborative autonomous architecture tailored for U-Healthcare, featuring: (i) a novel blockchain-edge co-design model; (ii) a lightweight 1D-CNN deployed at the edge for real-time multi-class arrhythmia classification (98.6% accuracy); and (iii) a secure, efficient on-chain/off-chain hybrid data sharing mechanism supporting fine-grained access control and end-device privacy preservation. Contribution/Results: Experimental evaluation demonstrates sub-80 ms anomaly alerting latency, 37% reduction in memory overhead, and significant improvements in security, real-time responsiveness, and resource efficiency—addressing critical challenges in privacy-aware, low-latency healthcare edge computing.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Computer Vision: Multi-modal Vision

Application Category

• Security and Privacy: Blockchains and distributed ledgers
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Economics, Online Markets and Human Computation: Economic aspects of blockchain and cryptocurrencies

Abstract

Edge Intelligence (EI) serves as a critical enabler for privacy-preserving systems by providing AI-empowered computation and distributed caching services at the edge, thereby minimizing latency and enhancing data privacy. The integration of blockchain technology further augments EI frameworks by ensuring transactional transparency, auditability, and system-wide reliability through a decentralized network model. However, the operational architecture of such systems introduces inherent vulnerabilities, particularly due to the extensive data interactions between edge gateways (EGs) and the distributed nature of information storage during service provisioning. To address these challenges, we propose an autonomous computing model along with its interaction topologies tailored for privacy-critical and time-sensitive health applications. The system supports continuous monitoring, real-time alert notifications, disease detection, and robust data processing and aggregation. It also includes a data transaction handler and mechanisms for ensuring privacy at the EGs. Moreover, a resource-efficient one-dimensional convolutional neural network (1D-CNN) is proposed for the multiclass classification of arrhythmia, enabling accurate and real-time analysis of constrained EGs. Furthermore, a secure access scheme is defined to manage both off-chain and on-chain data sharing and storage. To validate the proposed model, comprehensive security, performance, and cost analyses are conducted, demonstrating the efficiency and reliability of the fine-grained access control scheme.

Problem

• Addressing privacy and latency in U-Healthcare using blockchain and edge intelligence
• Securing data interactions in decentralized edge gateway networks
• Enabling real-time arrhythmia classification with resource-efficient 1D-CNN

Innovation

• Blockchain enhances edge intelligence for privacy
• 1D-CNN enables real-time arrhythmia classification
• Secure access scheme manages on-off-chain data