Medical Image Encryption Using DNA Computing and a BiologicallyInspired PRNG for Healthcare Data Privacy

With the rise of digital healthcare, ensuring the confidentiality of medical data has become increasingly critical. The widespread adoption of electronic medical records requires robust encryption techniques to protect sensitive patient information from unauthorized access and cyberattacks. We propose a novel pseudorandom number generator (PRNG) inspired by the Hardy–Weinberg equilibrium principle, integrating cryptographic techniques with nonlinear dynamics through quadratic functions to enhance randomness and security. The generated sequences are applied within a medical image encryption framework. Specifically, DNA encoding is used to transform medical image data, while the proposed PRNG provides highly random diffusion keys. For the confusion stage, the advanced encryption standard substitution box is used to introduce permutation. The effectiveness of the proposed PRNG is validated through a comprehensive experimental evaluation, including the National Institute of Standards and Technology (NIST) statistical test suite, in which it successfully passes all tests across two trial sets consisting of 30 and 100 generated sequences, respectively. Furthermore, the performance of the proposed encryption scheme is evaluated on medical images through statistical analyzes. Comparative results with recent state-of-the-art medical image encryption methods indicate enhanced security properties, particularly in terms of statistical randomness, key sensitivity, and resistance to cryptographic attacks.