The fog-enabled healthcare IoT (F-HIoT) paradigm is impending for the prospective patient-healthcare applications. The fog nodes permit the smart medical wearables (SMWs) to upload the information with low latency using 5G services. The security risks associated with the distributed attributes of fog nodes may hamper the monitoring of real-time medical reports with privacy. Many authenticated key exchange techniques can be witnessed to strengthen the interactions among SMW devices, fog nodes, and cloud server on insecure channels; however these are either based on computation-intensive crypto-primitives, or fail to provide anonymity to the F-HIoT end users. In this scheme, we propose an ultralightweight anonymous authentication protocol (FA-SMW) leveraging low-cost crypto-primitive operations, i.e., Chebyshev chaotic map for setting up a mutually agreed session key among three entities. The security properties of FA-SMW are evaluated and analyzed under random oracle model and Canetti–Krawczyk (eCK) threat model. These findings suggest that the proposed protocol considerably meets the desired requirements in the fog-enabled F-HIoT system.

The healthcare industry is currently seeing a significant rise in the use of mobile devices. These devices not only provide ways for communication and sharing of multimedia information, such as clinical notes and medical records, but also offer new possibilities for people to detect, monitor, and manage their health from anywhere at any time. Digital health technologies have the potential to improve patient care by making it more efficient, effective, and cost-effective. Utilizing digital devices and technologies can have a positive impact on many health conditions. This research focuses on dysphonia, a change in the sound of the voice that affects around one-third of individuals at some point in their lives. Voice disorders are becoming more common, despite being often overlooked. Mobile healthcare systems can provide quick and efficient assistance for detecting voice disorders. To make these systems reliable and accurate, it is important to develop an algorithm that can classify intelligently healthy and pathological voices. To achieve this task, we utilized a combination of several datasets such as Saarbruecken voice dataset (SVD), the Massachusetts Eye and Ear Infirmary database (MEEI), and a few private datasets of various voices (healthy and pathological) Additionally, we applied multiple machine learning algorithms, including decision tree, random forest, and support vector machine, to evaluate and determine the most effective algorithm among them for the detection of voice disorders. The experimental analyses are performed in terms of sensitivity, accuracy, receiver operating characteristic area, specificity, F-score and recall. The results demonstrated that the support vector machine algorithm, depending on the features selected by using appropriate feature selection methods, proved to be the most accurate in detecting voice diseases.

A significant evolution in healthcare recently uses technological advancements to perform different activities, such as patient electronic medical records (EMRs) data gathering, preserving, processing, diagnosis, and handling. The adaptation of the Internet of Things (IoT) and cloud has further facilitated the enhancement of related healthcare systems, which can considerably improve data connectivity, accessibility, and exchange, which leads to a significant improvement in the quality of services to patients. Furthermore, scientific computations over data in transmission can be exposed to adversaries and may reveal private data for financial benefit. This article uses the Cheon-Kim-Kim-Song fully homomorphic encryption scheme and IOTA Tangle using masked authenticated messaging (MAM) protocol to provide secure communication between patient and doctor. CKKS-FHE-based data encryption provides data privacy, and secured EMRs sharing through IOTA Tangle guarantees data confidentiality. The performance of this work is analyzed in terms of encryption and decryption time, and payload sharing using MAM and NON-MAM protocols results in evidence of the effectiveness of the approach and improves overall security. The proposed scheme performs better overall computation time and performance than other relevant schemes. Further, the security analysis shows that the proposed system is resilient to data immutability and integrity, forward secrecy, and passive and active attacks.

The authenticated key agreement is one of the major security services that can be used to secure an Internet of Things (IoT) environment, where the devices collect the data and the data is then aggregated at the cloud server, and then a user needs to access the data stored at the server(s) securely. For this purpose, after a mutual authentication performed between a user and the accessed server, a session key needs to be established among them for secure communication. In this article, we design an efficient lattice-based authenticated key exchange protocol using ring-based version of learning with errors assumption for the IoT-enabled smart devices. The proposed protocol is basically a key exchange that uses the reconciliation mechanism. The detailed security analysis under the standard model has been performed along with the informal security analysis to show that the proposed protocol is robust against different attacks. We then simulate the proposed protocol under the NS-3 simulator to measure the network performance parameters like network throughput and latency. A comparative analysis shows that the proposed protocol has superior security, less computational cost, and comparable communication cost when compered these parameters with the other competing schemes.

Abstract In many cases, images contain sensitive information and patterns that require secure processing to avoid risk. It can be accessed by unauthorized users who can illegally exploit them to threaten the safety of people’s life and property. Protecting the privacies of the images has quickly become one of the biggest obstacles that prevent further exploration of image data. In this paper, we propose a novel privacy-preserving scheme to protect sensitive information within images. The proposed approach combines deoxyribonucleic acid (DNA) sequencing code, Arnold transformation (AT), and a chaotic dynamical system to construct an initial S-box. Various tests have been conducted to validate the randomness of this newly constructed S-box. These tests include National Institute of Standards and Technology (NIST) analysis, histogram analysis (HA), nonlinearity analysis (NL), strict avalanche criterion (SAC), bit independence criterion (BIC), bit independence criterion strict avalanche criterion (BIC-SAC), bit independence criterion nonlinearity (BIC-NL), equiprobable input/output XOR distribution, and linear approximation probability (LP). The proposed scheme possesses higher security wit NL = 103.75, SAC ≈ 0.5 and LP = 0.1560. Other tests such as BIC-SAC and BIC-NL calculated values are 0.4960 and 112.35, respectively. The results show that the proposed scheme has a strong ability to resist many attacks. Furthermore, the achieved results are compared to existing state-of-the-art methods. The comparison results further demonstrate the effectiveness of the proposed algorithm.

The Internet of Things (IoT) devices, networks, and applications have become an integral part of modern societies. Despite their social, economic, and industrial benefits, these devices and networks are frequently targeted by cybercriminals. Hence, IoT applications and networks demand lightweight, fast, and flexible security solutions to overcome these challenges. In this regard, artificial-intelligence-based solutions with Big Data analytics can produce promising results in the field of cybersecurity. This article proposes a lightweight dense random neural network (DnRaNN) for intrusion detection in the IoT. The proposed scheme is well suited for implementation in resource-constrained IoT networks due to its inherent improved generalization capabilities and distributed nature. The suggested model was evaluated by conducting extensive experiments on a new generation IoT security dataset ToN_IoT. All the experiments were conducted under different hyperparameters and the efficiency of the proposed DnRaNN was evaluated through multiple performance metrics. The findings of the proposed study provide recommendations and insights in binary class and multiclass scenarios. The proposed DnRaNN model attained attack detection accuracy of 99.14% and 99.05% for binary class and multiclass classifications, respectively.

In critical domains such as medical and military, reversible watermarking (RW) has been used. In the medical domain, different modalities are used to store patient information. The current study focuses on the application of RW in echocardiography data. Mostly, RW is applied to protect patient data without affecting the quality of the decoded image. The RW methods are benchmarked as per imperceptibility, robustness, and payload. The survey presents a comparison of state-of-the-art RW techniques. The imperceptibility and payload are balanced through a tradeoff. It has been observed in the literature that most of the RW methods lack robustness, and very small-scale robustness has been achieved in this domain of watermarking. Different types of RW, i.e., fragile, semifragile, and robust methods, are being compared and reviewed. Mostly, fragile methods are developed on the error-expansion techniques built on histogram shifting-based approach. In this study, several RW methods are compared and the results are presented.

Substitution boxes are the only nonlinear component of the symmetric key cryptography and play a key role in the cryptosystem. In block ciphers, the S-boxes create confusion and add valuable strength. The majority of the substitution boxes algorithms focus on bijective Boolean functions and primitive irreducible polynomial that generates the Galois field. For binary field F2, there are exactly 16 primitive irreducible polynomials of degree 8 and it prompts us to construct 16 Galois field extensions of order 256. Conventionally, construction of affine power affine S-box is based on Galois field of order 256, depending on a single degree 8 primitive irreducible polynomial over ℤ 2 . In this manuscript, we study affine power affine S-boxes for all the 16 distinct degree 8 primitive irreducible polynomials over ℤ 2 to propose 16 different 8 × 8 substitution boxes. To perform this idea, we introduce 16 affine power affine transformations and, for fixed parameters, we obtained 16 distinct S-boxes. Here, we thoroughly study S-boxes with all possible primitive irreducible polynomials and their algebraic properties. All of these boxes are evaluated with the help of nonlinearity test, strict avalanche criterion, bit independent criterion, and linear and differential approximation probability analyses to measure the algebraic and statistical strength of the proposed substitution boxes. Majority logic criterion results indicate that the proposed substitution boxes are well suited for the techniques of secure communication.

Substitution box ( S -box), being the only nonlinear component, contributes to the confusion creating capability of a cryptosystem. Keeping in view the predominant role of S -box, many design algorithms to synthesize cryptographically stronger S -boxes have gained pivotal attention. A quick review of these algorithms shows that all these ideas mainly concentrate on the choice of bijective Boolean functions, with nonobservance to the irreducible polynomial that generates the Galois field. In this paper, we propose that the selection of irreducible polynomial has a deep influence on the highly desirable features of an S -box such as nonlinearity, strict avalanche, bit independence, linear approximation probability, and differential approximation probability. We underpin our claim by investigating a detailed model, which deploys the same algorithm but different polynomials and produces unusual changes in the results regarding the performance parameters of S -box.