Reconfigurable security solution based on hopfield neural network for e-healthcare applications

In the healthcare sector, e-diagnosis through medical images is essential in a multi-speciality hospital; securing the medical images becomes crucial for preserving an individual's privacy in e-healthcare applications. So, this paper has proposed a novel encryption scheme implemented on reconfigurable hardware. Realising image encryption schemes on FPGA hardware platforms offers substantial advantages over software implementations. The image-specific key and Hopfield Neural Network (HNN) carry out the diffusion process using the suggested encryption method. The confusion is accomplished simultaneously by the pseudo-random memory addresses derived via a 16-bit stream cipher circuit, which incorporates cryptography and neural network dynamics methods. By breaking up the spatial redundancy in image data, this diffusion mechanism increases the data's resilience against statistical attacks, yielding an average entropy of 7.99 and near zero correlation. When implemented on an FPGA platform, this dual-layer encryption technique's fast processing speed, parallelism, and reconfigurability are substantial benefits, especially for real-time and resource-constrained applications. FPGA implementation occupies 20% of the total hardware and 424.71 mW of power dissipation on Intel Cyclone V FPGA.