A NOVEL DATA SECURE MODEL FOR INTERNET OF HEALTH THINGS WITH A NEW LIGHTWEIGHT CRYPTOGRAPHY ALGORITHM AND STEGANOGRAPHY TECHNIQUE

Ensuring the security of data in Internet of Things (IoT) based healthcare systems (HS) presents considerable challenges due to the limitations of traditional embedding methods and cryptography techniques, leading to more memory consumption, more execution time, less security, inadequate payload capacity, and performance inefficiencies. To address these issues, the Bernoulli Fish-based Stego Algorithm (BFBSA) is introduced as an innovative solution. Specifically designed for IoT healthcare data, this algorithm is validated through the encryption and embedding of healthcare data. The process involves initializing IoT healthcare data, encrypting it using the BFBSA algorithm, and embedding the encrypted data within steganographic images. Performance analysis is conducted using key metrics such as payload capacity, encryption time, memory usage, PSNR, and MSE. Comparative analysis with existing approaches highlights the BFBSA model’s efficiency and its effectiveness in ensuring secure and optimized data management in IoT healthcare environments.