An Adaptive Lightweight Hybrid Encryption Scheme for Securing the Healthcare Data in Cloud-Assisted Internet of Things

The revolution of IoT systems surpasses daily human facilities for providing financial, mechanical and social aspects. However, the secure transmission of health data over the Internet is a challenging task. Thus, to solve this issue, the proposed study presents the security of medical images in IOT through a new Lightweight Hybrid Encryption (LHE) method with optimization strategies. Initially, the input medical images are encrypted to access the data with higher security through an efficient substitution box (S-box) block cipher and elliptic curves. The proposed encryption scheme assists in minimizing the computational time to several extents by utilizing the Finite Elliptic Curves (FEC) of smaller sizes to generate the S-boxes. Then, a cover image is chosen to hide the information or the confidential images with different pixel sizes. The cover image is partitioned into several non-overlapping blocks. From this, an optimal block is selected by using an adaptive COOT optimization algorithm. After selecting the best block, the cipher image is decomposed and is concealed with the selected block through a Least Significant Bit (LSB) mechanism. Finally, the encrypted medical image data are securely stored in the cloud storage platform through the Internet. The simulation results show that the proposed model obtains better results in terms of security level (97.82%), encryption time (15.4 s), minimum energy consumption (1.33 pJ/bit) and better execution time (18.41 s) related to the data size (bits).