A Secure and Privacy-Preserving Framework for Real-Time Data Streams in Telemedicine
Murugan Lakshmanan · 2025
The increasing reliance on telemedicine for realtime healthcare services necessitates robust security and privacy-preserving mechanisms to protect sensitive patient data. This study proposes a multi-layered security framework integrating Attribute-Based Homomorphic Encryption (ABHE), Secure Multi-Party Computation (SMPC), Blockchain Smart Contracts, and Quantum-Resistant Cryptography to ensure secure encrypted computations, decentralized access control, and long-term data protection. ABHE enables better access control and privacy-preserving computations, while SMPC facilitates secure collaborative analytics among multiple healthcare providers without revealing individual inputs. Blockchain-based smart contracts autonomously manage access permissions, ensuring tamper-proof audit. Additionally, quantum-resistant cryptographic techniques safeguard encrypted data against emerging quantum threats, further enhancing the framework’s security. Encryption performance is assessed based on encryption speed, security strength, and tamper detection accuracy, achieving 99.8% tamper detection accuracy for 200 KB data size while maintaining an encryption speed of $2.76 \mathrm{~KB} / \mathrm{ms}$. The SMPC execution analysis evaluates computation time, threshold decryption time, and unauthorized access prevention, demonstrating 99.8% accuracy in secure computations with an increase in processing time as more participants are involved. Blockchain performance is measured through transaction latency, audit log integrity, and storage efficiency, showing 100% audit log integrity and execution latency below 120 ms, ensuring real-time access control enforcement. The results confirm that the proposed privacypreserving security framework achieves an optimal balance between encryption efficiency, data integrity, and access control scalability for real-time telemedicine applications. By integrating advanced encryption, blockchain, and quantumresistant techniques, this study offers a comprehensive and future-proof solution for secure and privacy preserving healthcare data streams, ensuring suitability for large-scale adoption in privacy-sensitive medical application.