Enhancing medical image privacy in IoT with bit-plane level encryption using chaotic map.

Area of Science:

• Medical Imaging
• Cybersecurity
• Internet of Things (IoT)

Background:

• Privacy preservation is crucial for medical imaging, particularly on resource-constrained IoT devices.
• Existing encryption methods may not be efficient for edge computing environments.

Purpose of the Study:

• To develop a novel, bit plane-level encryption method for medical images tailored for IoT environments.
• To enhance the security and privacy of sensitive medical data transmitted and stored on smart devices.

Main Methods:

• Utilized Secure Hash Algorithm (SHA) and Chen chaotic map for generating random number vectors.
• Employed bit plane shuffling and diffusion using generated random vectors.
• Embedded the encrypted image into a carrier image for visual security.

Main Results:

• Demonstrated high resilience to attacks through correlation coefficient, histogram, and entropy analyses.
• Achieved a large key space of (10^90)^8 and strong sensitivity.
• Evaluated performance via occlusion analysis, confirming robustness.

Conclusions:

• The proposed encryption scheme is highly resilient and suitable for resource-limited IoT devices.
• Effectively addresses privacy concerns in medical imaging within the IoT context.