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Digital images are being frequently used for diagnosis in clinics today. Diagnostic images with identifying patient data are stored and transmitted across open networks. Security measures are therefore becoming more and more crucial to safeguard the confidentiality of sensitive patient data. In medical imaging, adjacent pixels show a strong degree of connection. This paper presents an efficient intra-inter bit-level pixel operation-based image encryption method that eliminates pixel correlation and provides strong security. In this method, after gathering medical image data from IoT sensors, the intra bit-level pixel permutation and diffusion operations are executed separately and in parallel in each of the bit planes, and the inter bit-level pixel permutation and diffusion operations are then performed in the concatenated bit-planes. The bit-level operation not only permutes but also diffuses the pixels at the same time. As a result, the security of the algorithm is increased by executing several pixel permutations and diffusions. In addition to this, the approach is effective due to the parallel execution of bit-plane permutation and diffusion. The simulation results and the security analysis indicate the superior performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The new era of information technologies made smart healthcare by including edge, Internet of Things (loT), Internet of Medical Things (IoMT), cloud computing etc. This adaptation has partially replaced the old medical system, making healthcare more efficient, easy, and customized. Wireless medical sensors are used in the smart healthcare sector to capture the patient's health data and transmit it to the remote server for treatment or diagnostic purposes. Every time a cloud-based solution is adopted to collect and preserve the sensitive and personal health information. This collected patient data is shared between the different stakeholders across the network. Due to this, healthcare systems are vulnerable to confidentiality, integrity and privacy threats. Secure data access is a big challenge in such systems. In this paper, a lightweight authentication technique with the privacy-preserving schema using fully homomorphic encryption is proposed. This approach can be used for a secure access of patient data through online mode, and also the data can be shared in an encrypted format for desired purposes of stakeholders. The proposed authentication scheme is for Internet of Medical Things that can resist all network attacks and is easy to implement. The performance analysis of the proposed work shows that overall execution time (80.03%), communication cost (15.62%) and energy consumption (79.19%) is reduced in comparison with existing work. [Display omitted] • A lightweight mutual authentication scheme for smart healthcare systems is designed and implemented using on login ID, password, and perceptual hash based biometric security. • A formal security analysis is introduced using Burrows–Abadi–Needham (BAN) logic that resists all well-known attacks and provide anonymity of patient identity. • A performance comparison of the proposed scheme with existing schemes is also carried out in terms of security features and communication overheads, computation overheads, storage and network throughput. [ABSTRACT FROM AUTHOR]

• A novel deep learning models for arrhythmia detection in IoT healthcare applications are proposed. • It has the independence of the ECG signal quality, as operation is on the spectrograms of the ECG signals. • Ability to work on a single ECG lead, which reduces the complexity of operations. • Applicability of the proposed framework in the smart healthcare platform for real-time operation. • The proposed framework overcomes the overfitting problems and achieves high accuracy on several datasets. In this paper, novel convolutional neural network (CNN) and convolutional long short-term (ConvLSTM) deep learning models (DLMs) are presented for automatic detection of arrhythmia for IoT applications. The input ECG signals are represented in 2D format, and then the obtained images are fed into the proposed DLMs for classification. This helps to overcome most of the problems of the previous machine and deep learning models such as overfitting, and working on more than one lead of ECG signals. We use several publicly available datasets from PhysioNet such as MIT-BIH, PhysioNet 2016 and PhysioNet 2018 for model assessment. Overall accuracies of 97%, 98 %, 94 % and 91 % are obtained on spectrograms of MIT-BIH dataset, compressed MIT-BIH dataset, PhysioNet 2016 dataset, and PhysioNet 2018 dataset, respectively. Compared to the previous works, the proposed framework is more robust and efficient, especially in the case of noisy data. Deep Learning Models for Arrhythmia Detection in IoT Healthcare Applications. [Display omitted] [ABSTRACT FROM AUTHOR]

Critical infrastructures (CIs) include the vital resources for the country's economic and health systems and should be kept secure. We face improvements in the Internet of Things which brings benefits and, at the same time, dependency for CIs. Internet of Medical Things (IoMT) is among the CI sectors that gather health-related information from patients via sensors and provide healthcare services accordingly. However, research has highlighted that this large-scale system opens the door to the patients' private data disclosure. Recent work has concentrated on proposing authentication schemes to address this challenge. Motivated by this, in this paper, we introduce a secure and lightweight authentication and key agreement model named Slight. We informally prove Slight's security and robustness against attacks and formally by using the Scyther tool. We analyze Slight's performance to show it causes minimal computational overhead (0.0076 ms) and comparable communication overhead (1632 bits), making it suitable for IoMT. [Display omitted] • We propose secure and lightweight authentication and key agreement model named Slight for IoMT environments to provide privacy and security of patients' health-related data. • The security of the proposed protocol is validated formally through the Scyther tool. • The proposed protocol provides resistance against well-known potential security attacks. • The performance analysis showed that proposed protocol is more efficient than other competing protocols in terms of computational and communication overhead. [ABSTRACT FROM AUTHOR]

• A novel method for secured ECG transmission to cardiologist for clinical healthcare has been proposed. • 3-DES has been implemented for encryption and water cycle optimization (WCO) algorithm is employed for authentication. • The encrypted and authenticated ECG features are transmitted to cardiologist for analysis via an IoT platform. • The significant of the proposed technique attains better value of results as compared to the existing methods. Telecardiology offers early detection, consultations and disease management in Golden Hours to patients. Transmitting ECG data to a cardiologist without security may incline to misdiagnosis. In this paper, a novel method for secured ECG transmission to cardiologist for clinical healthcare has been proposed. Firstly, ECG datasets are taken from the MIT-BIH database and pre-processed. Savitzky-Golay (SG) filter has been used for the removal of low-frequency noise, while high frequency noise is eliminated using maximal overlap wavelet packet transform (MOWPT) followed by features extraction. For security, triple data encryption standard (3-DES) has been implemented for encryption and water cycle optimization (WCO) algorithm is employed for authentication. Then encrypted and authenticated ECG features are transmitted to cardiologist for analysis via ThingSpeak, an internet of things (IoT) platform. The performance of the proposed method is calculated viz. avalanche effect is 50.12% and mean square error is 0.463, number of pixels change rate (NPCR) is 100, unified averaged changed intensity (UACI) is 39.698 and execution time is 0.003 msec. The significant of the proposed technique provides minimum error with less processing time, better values of avalanche effect, NCPR and UACI as compared to the existing methods. [ABSTRACT FROM AUTHOR]