Optimal multiple key‐based homomorphic encryption with deep neural networks to secure medical data transmission and diagnosis

• Published in: Expert systems Vol. 39; no. 4
• Main Authors: Alzubi, Jafar A., Alzubi, Omar A., Beseiso, Majdi, Budati, Anil Kumar, Shankar, K.
• Format: Journal Article
• Language: English
• Published: Oxford Blackwell Publishing Ltd 01.05.2022
• Subjects: Activity recognition; Algorithms; Artificial neural networks; Chemical reactions; Classification; Data transmission; Datasets; deep learning; Diagnosis; Homomorphic encryption; Machine learning; Metaheuristics; Neural networks; optimal key generation; Optimization; sailfish optimizer; security; Sleep
• ISSN: 0266-4720; 1468-0394
• DOI: 10.1111/exsy.12879

Medical database classification problems can be considered as complex optimization problems to assure the diagnosis support precisely. In healthcare, several computer researchers have employed different deep learning (DL) approaches to enhance the classification performance. Besides, encryption is an effective way to offer secure transmission of medical data over public network. With this motivation, this paper presents new privacy‐preserving encryption with DL based medical data transmission and classification (PPEDL‐MDTC) model. The presented model derives multiple key‐based homomorphic encryption (MHE) technique with sailfish optimization (SFO), called MHE‐SFO algorithm‐based encryption process. In addition, the cross‐entropy based artificial butterfly optimization‐based feature selection technique and optimal deep neural network (ODNN) based classification is carried out. In ODNN model, the hyperparameter optimization of the DNN model is carried out utilizing the use of chemical reaction optimization (CRO) algorithm. The proposed method has been simulated utilizing Python 3.6.5 tool, which is tested using activity recognition and sleep stage dataset. A detailed comparative outcomes analysis makes sure the higher efficiency of the PPEDL‐MDTC on the state of art techniques with the detection accuracy of 0.9813 and 0.9650 on the applied activity recognition and University College Dublin Sleep Stage dataset.