Selvi P, Sakthivel S
Department of Computer Science and Engineering, Research scholar, Anna University, Chennai, Tamil Nadu, India.
Department of Computer Science and Engineering, Sona College of Technology, Salem, Tamil Nadu, India.
Sci Rep. 2025 Aug 22;15(1):30867. doi: 10.1038/s41598-025-01315-5.
In digital healthcare, ensuring the privacy and security of sensitive mental health data remains a critical challenge. This paper introduces SymECCipher, a novel hybrid encryption framework that integrates Elliptic Curve Cryptography (ECC) for key exchange and the Advanced Encryption Standard (AES) for data encryption. Unlike conventional encryption models such as RSA-2048 (15ms encryption, 12ms decryption) and AES-256 (6ms encryption, 5ms decryption), SymECCipher achieves significantly lower encryption time (5ms) and decryption time (4ms) while maintaining a high throughput of 1000 Mbps, ensuring secure and efficient data encryption. The proposed methodology is designed to handle secure cloud-based healthcare applications, implemented in the form of User, Doctor, and Cloud Modules to handle patient records and treatment recommendations. This model addresses existing encryption inefficiencies by balancing high-speed cryptographic operations with robust data security, making it suitable for real-time medical data storage and retrieval. Statistical analysis confirms its superior performance, demonstrating a 25-40% reduction in computational overhead compared to traditional cryptosystems. Furthermore, this work outlines the integration of machine learning (ML)-based depression detection within the encrypted framework, ensuring privacy-preserving data analysis. The results highlight SymECCipher's potential for large-scale healthcare deployment, offering a scalable, quantum-resistant, and blockchain-compatible encryption framework. Future research can be extended by integrating lattice-based cryptography, to enhance quantum security and extending SymECCipher's applicability to wearable health devices and telemedicine platforms.
在数字医疗保健领域，确保敏感心理健康数据的隐私和安全仍然是一项严峻挑战。本文介绍了SymECCipher，这是一种新颖的混合加密框架，它集成了用于密钥交换的椭圆曲线密码学（ECC）和用于数据加密的高级加密标准（AES）。与诸如RSA - 2048（加密15毫秒，解密12毫秒）和AES - 256（加密6毫秒，解密5毫秒）等传统加密模型不同，SymECCipher在保持1000 Mbps高吞吐量的同时，实现了显著更低的加密时间（5毫秒）和解密时间（4毫秒），确保了安全高效的数据加密。所提出的方法旨在处理基于云的安全医疗保健应用，以用户、医生和云模块的形式实现，用于处理患者记录和治疗建议。该模型通过在高速加密操作与强大的数据安全性之间取得平衡，解决了现有的加密效率低下问题，使其适用于实时医疗数据存储和检索。统计分析证实了其卓越的性能，与传统密码系统相比，计算开销降低了25 - 40%。此外，这项工作概述了基于机器学习（ML）的抑郁症检测在加密框架内的集成，确保了隐私保护数据分析。结果突出了SymECCipher在大规模医疗保健部署中的潜力，提供了一个可扩展、抗量子且与区块链兼容的加密框架。未来的研究可以通过集成基于格的密码学来扩展，以增强量子安全性，并将SymECCipher的适用性扩展到可穿戴健康设备和远程医疗平台。
