An efficient medical image encryption algorithm for telemedicine applications

Daoud, 2019, TACRM: trust access control and resource management mechanism in fog computing, Hum.-Cent. Comput. Inf. Sci., 9, 1, 10.1186/s13673-019-0188-3 Megouache, 2020, Ensuring user authentication and data integrity in multi-cloud environment, Hum-Cent.Comput. Inf. Sci., 10, 1, 10.1186/s13673-020-00224-y Yang, 2020, Low-latency cloud-fog network architecture and its load balancing strategy for medical big data, J. Ambient Intell. Humaniz. Comput., 1 Pham, 2020, A survey of multi-access edge computing in 5G and beyond: fundamentals, technology integration, and state-of-the-art, IEEE Access, 8, 116974, 10.1109/ACCESS.2020.3001277 Blanquer, 2020, Federated and secure cloud services for building medical image classifiers on an intercontinental infrastructure, Fut. Gener. Comput. Syst., 10.1016/j.future.2020.04.012 Marwan, 2018, Security Enhancement in Healthcare Cloud using Machine Learning, Procedia Comput. Sci., 127, 388, 10.1016/j.procs.2018.01.136 Pirbhulal, 2019, A joint resource-aware and medical data security framework for wearable healthcare systems, Fut. Gener. Comput. Syst., 10.1016/j.future.2019.01.008 Bhargavi, 2019, Security of Medical Big Data Images using Decoy Technique Al Hamid, 2017, A security model for preserving the privacy of medical big data in a healthcare cloud using a fog computing facility with pairing-based cryptography, IEEE Access, 5, 22313, 10.1109/ACCESS.2017.2757844 HAN, 2019 Vengadapurvaja, 2017, An efficient homomorphic medical image encryption algorithm for cloud storage security, Procedia Comput. Sci., 115, 643, 10.1016/j.procs.2017.09.150 Mim, 2022, A review on disease detection from medical images using machine learning, 1437 Islam, 2022, A secure framework toward IoMT-assisted data collection, modeling, and classification for intelligent dermatology healthcare services, Contrast Media Mol. Imag., 2022, 10.1155/2022/6805460 Luo, 2022, Semi-supervised medical image segmentation via uncertainty rectified pyramid consistency, Med. Image Anal., 80, 10.1016/j.media.2022.102517 Tang, 2022, Unified medical image segmentation by learning from uncertainty in an end-to-end manner, Knowl.-Based Syst., 241, 10.1016/j.knosys.2022.108215 Popescu, 2022, Obfuscation algorithm for privacy-preserving deep learning-based medical image analysis, Appl. Sci., 12, 3997, 10.3390/app12083997 Daemen, 2001, Reijndael: the advanced encryption standard, Dr. Dobb's J. Softw. Tools Prof. Program., 26, 137 Parameshachari, 2022, Medical image encryption using SCAN technique and chaotic tent map system, 181 Sang, 2022, Image encryption based on logistic chaotic systems and deep autoencoder, Pattern Recognit. Lett., 153, 59, 10.1016/j.patrec.2021.11.025 Nawarathne, 2022, Comprehensive study on denoising of medical images utilizing neural network-based autoencoder, 159 Zhao, 2023 Chun, 2022, Fully homomorphic encryption scheme and Fermat's little theorem, J. Algebra Appl. Kumar, 2022, Blockchain and homomorphic encryption based privacy-preserving model aggregation for medical images, Computer. Med. Imag. Graph., 102, 10.1016/j.compmedimag.2022.102139 Kiran, 2022, Resource optimized selective image encryption of medical images using multiple chaotic systems, Microprocess. Microsyst., 91, 10.1016/j.micpro.2022.104546 Ahmad, 2012, Efficiency analysis and security evaluation of image encryption schemes, Int. J. Video Image Process. Netw. Secur., 12, 18 Wu, 2011, NPCR and UACI randomness tests for image encryption, 31 Som, 2019, A selective bitplane image encryption scheme using chaotic maps, Multimed. Tools Appl., 78, 10373, 10.1007/s11042-018-6539-7 Darwish, 2019, 78, 19229 Rehman, 2015, Selective encryption for gray images based on chaos and DNA complementary rules, Multimed. Tools Appl., 74, 4655, 10.1007/s11042-013-1828-7 Ullah, 2013, Selective region based images encryption, 125