A hybrid ECC-AES encryption framework for secure and efficient cloud-based data protection.

Area of Science:

• Digital Healthcare Security
• Cryptographic Frameworks
• Data Privacy in Medicine

Background:

• Securing sensitive mental health data in digital healthcare is a significant challenge.
• Existing encryption methods present inefficiencies in speed and computational overhead.
• There is a need for robust, efficient encryption solutions for cloud-based healthcare applications.

Purpose of the Study:

• To introduce SymECCipher, a novel hybrid encryption framework for digital mental health data.
• To evaluate SymECCipher's performance against conventional encryption models.
• To demonstrate the framework's suitability for secure cloud-based healthcare applications and privacy-preserving data analysis.

Main Methods:

• Integration of Elliptic Curve Cryptography (ECC) for key exchange and Advanced Encryption Standard (AES) for data encryption.
• Development of User, Doctor, and Cloud Modules for managing patient records and treatment recommendations.
• Implementation of machine learning (ML)-based depression detection within the encrypted framework.

Main Results:

• SymECCipher achieved significantly lower encryption (5ms) and decryption (4ms) times compared to RSA-2048 and AES-256.
• The framework demonstrated a high throughput of 1000 Mbps, ensuring efficient data handling.
• Statistical analysis confirmed a 25-40% reduction in computational overhead, showcasing superior performance.

Conclusions:

• SymECCipher provides a scalable, quantum-resistant, and blockchain-compatible solution for secure digital mental health data.
• The framework enhances real-time medical data storage and retrieval efficiency.
• SymECCipher shows potential for large-scale healthcare deployment, including integration with ML for privacy-preserving analysis.