IEFHAC: Image encryption framework based on hessenberg transform and chaotic theory for smart health.

Area of Science:

• Computer Science
• Information Security
• Medical Informatics

Background:

• Smart cities leverage technology to enhance quality of life, with smart healthcare systems being a key innovation area.
• Secure and efficient management of big data in healthcare, particularly high-resolution medical images (80% of medical data), is critical.
• Existing security frameworks require robust encryption methods to protect sensitive patient information in medical images from unauthorized access.

Purpose of the Study:

• To develop and present a novel Image Encryption Framework based on Hessenberg transform and Chaotic encryption (IEFHAC).
• To improve the security and reduce the computational time for encrypting sensitive patient data, specifically medical images.
• To enhance the resilience of smart healthcare systems against unauthorized access and statistical attacks.

Main Methods:

• The proposed Image Encryption Framework based on Hessenberg transform and Chaotic encryption (IEFHAC) utilizes two 1D-chaotic maps: Logistic map and Sine map for data confusion.
• Diffusion is achieved by applying the Hessenberg household transform.
• The Logistic and Sine maps interact dynamically, altering key parameters for enhanced security.

Main Results:

• IEFHAC demonstrates superior performance with high NPCR (99.66% to 100%) and UACI (37.39%).
• The framework achieves a reduced computational time of 0.36 seconds for encryption.
• Experimental analysis confirms IEFHAC's robustness against statistical attacks.

Conclusions:

• IEFHAC offers an effective solution for securing sensitive medical images in big-data smart healthcare systems.
• The proposed method balances high security with efficient processing, crucial for real-time medical applications.
• This framework contributes to the advancement of secure data management in the evolving landscape of smart cities.