A privacy-preserving cryptosystem for IoT E-healthcare

Securing health care data through blockchain enabled collaborative machine learning

Transferring of data in machine learning from one party to another party is one of the issues that has been in existence since the development of technology. Health care data collection using machine learning techniques can lead to privacy issues which cause disturbances among the parties and reduces the possibility to work with either of the parties. Since centralized way of information transfer between two parties can be limited and risky as they are connected using machine learning, this factor motivated us to use the decentralized way where there is no connection but model transfer between both parties will be in process through a federated way. The purpose of this research is to investigate a model transfer between a user and the client(s) in an organization using federated learning techniques and reward the client(s) for their efforts with tokens accordingly using blockchain technology. In this research, the user shares a model to organizations that are willing to volunteer their service to provide help to the user. The model is trained and transferred among the user and the clients in the organizations in a privacy preserving way. In this research, we found that the process of model transfer between user and the volunteered organizations works completely fine with the help of federated learning techniques and the client(s) is/are rewarded with tokens for their efforts. We used the COVID-19 dataset to test the federation process, which yielded individual results of 88% for contributor a, 85% for contributor b, and 74% for contributor c. When using the FedAvg algorithm, we were able to achieve a total accuracy of 82%.

Security of medical images for telemedicine: a systematic review

Recently, there has been a rapid growth in the utilization of medical images in telemedicine applications. The authors in this paper presented a detailed discussion of different types of medical images and the attacks that may affect medical image transmission. This survey paper summarizes existing medical data security approaches and the different challenges associated with them. An in-depth overview of security techniques, such as cryptography, steganography, and watermarking are introduced with a full survey of recent research. The objective of the paper is to summarize and assess the different algorithms of each approach based on different parameters such as PSNR, MSE, BER, and NC.

Ensemble of Deep Learning Based Clinical Decision Support System for Chronic Kidney Disease Diagnosis in Medical Internet of Things Environment

Recently, Internet of Things (IoT) and cloud computing environments become commonly employed in several healthcare applications by the integration of monitoring things such as sensors and medical gadgets for observing remote patients. For availing of improved healthcare services, the huge count of data generated by IoT gadgets from the medicinal field can be investigated in the CC environment rather than relying on limited processing and storage resources. At the same time, earlier identification of chronic kidney disease (CKD) becomes essential to reduce the mortality rate significantly. This study develops an ensemble of deep learning based clinical decision support systems (EDL-CDSS) for CKD diagnosis in the IoT environment. The goal of the EDL-CDSS technique is to detect and classify different stages of CKD using the medical data collected by IoT devices and benchmark repositories. In addition, the EDL-CDSS technique involves the design of Adaptive Synthetic (ADASYN) technique for outlier detection process. Moreover, an ensemble of three models, namely, deep belief network (DBN), kernel extreme learning machine (KELM), and convolutional neural network with gated recurrent unit (CNN-GRU), are performed. Finally, quasi-oppositional butterfly optimization algorithm (QOBOA) is used for the hyperparameter tuning of the DBN and CNN-GRU models. A wide range of simulations was carried out and the outcomes are studied in terms of distinct measures. A brief outcomes analysis highlighted the supremacy of the EDL-CDSS technique on exiting approaches.

IEFHAC: Image encryption framework based on hessenberg transform and chaotic theory for smart health

Smart cities aim to improve the quality of life by utilizing technological advancements. One of the main areas of innovation includes the design, implementation, and management of data-intensive medical systems also known as big-data Smart Healthcare systems. Smart health systems need to be supported by highly efficient and resilient security frameworks. One of the important aspects that smart health systems need to provide, is timely access to high-resolution medical images, that form about 80% of the medical data. These images contain sensitive information about the patient and as such need to be secured completely. To prevent unauthorized access to medical images, the process of image encryption has become an imperative task for researchers all over the world. Chaos-based encryption has paved the way for the protection of sensitive data from being altered, modified, or hacked. In this paper, we present an Image Encryption Framework based on Hessenberg transform and Chaotic encryption (IEFHAC), for improving security and reducing computational time while encrypting patient data. IEFHAC uses two 1D-chaotic maps: Logistic map and Sine map for the confusion of data, while diffusion has been achieved by applying the Hessenberg household transform. The Sin and Logistic maps are used to regeneratively affect each other's output, as such dynamically changing the key parameters. The experimental analysis demonstrates that IEFHAC shows better results like NPCR ranging from 99.66 to 100%, UACI of 37.39%, lesser computational time of 0.36 s, and is more robust to statistical attacks.

Revolutionary Strategies Analysis and Proposed System for Future Infrastructure in Internet of Things

The Internet of Things (IoT) has changed the worldwide network of people, smart devices, intelligent things, data, and information as an emergent technology. IoT development is still in its early stages, and numerous interrelated challenges must be addressed. IoT is the unifying idea of embedding everything. The Internet of Things offers a huge opportunity to improve the world’s accessibility, integrity, availability, scalability, confidentiality, and interoperability. However, securing the Internet of Things is a difficult issue. The IoT aims to connect almost everything within the framework of a common infrastructure. This helps in controlling devices and, will allow device status to be updated everywhere and at any time. To develop technology via IoT, several critical scientific studies and inquiries have been carried out. However, many obstacles and problems remain to be tackled in order to reach IoT’s maximum potential. These problems and concerns must be taken into consideration in different areas of the IoT, such as implementation in remote areas, threats to the system, development support, social and environmental impacts, etc. This paper reviews the current state of the art in different IoT architectures, with a focus on current technologies, applications, challenges, IoT protocols, and opportunities. As a result, a detailed taxonomy of IoT is presented here which includes interoperability, scalability, security and energy efficiency, among other things. Moreover, the significance of blockchains and big data as well as their analysis in relation to IoT, is discussed. This article aims to help readers and researchers understand the IoT and its applicability to the real world.

A Security Management Framework for Big Data in Smart Healthcare

Big Data analytics in the medical sector can assist medical professionals to facilitate improvement in healthcare. With the help of data analysis, clinical images of patients can be used to detect certain medical conditions. In the COVID-19 pandemic, many integrated technologies are being used to remodel the healthcare systems. The management of an integrated healthcare solution necessitates the need for security of the medical data. In this paper, we propose a security framework based on the Logistic equation, Hyperchaotic equation, and Deoxyribonucleic Acid (DNA) encoding. Subsequently, a Lossless Computational Secret Image Sharing (CSIS) method is used to convert the encrypted secret image into shares for distributed storage in cloud-based servers. Hyperchaotic and DNA encryption is performed to improve the overall security of the system. Furthermore, Pseudorandom Numbers (PRN) generated by the logistic equation are XORed with the image sequence in two phases by changing the parameters slightly. Finally, the application of Secret Sharing (SS) generates completely noise-like cipher images that enhance the security of the cloud-based cryptosystem. The generated shares are small in size and require fewer resources like storage capacity and transmission bandwidth which is highly desirable in IoT-based systems. It is verified that the cryptosystem is highly secure against attacks as well as interferences and has a very strong key-sensitivity.

Privacy-Accuracy Consideration in Devices That Collect Sensor-Based Information

Accurately tailored support such as advice or assistance can increase user satisfaction from interactions with smart devices; however, in order to achieve high accuracy, the device must obtain and exploit private user data and thus confidential user information might be jeopardized. We provide an analysis of this privacy-accuracy trade-off. We assume two positive correlations: a user's utility from a device is positively correlated with the user's privacy risk and also with the quality of the advice or assistance offered by the device. The extent of the privacy risk is unknown to the user. Thus, privacy concerned users might choose not to interact with devices they deem as unsafe. We suggest that at the first period of usage, the device should choose not to employ the full capability of its advice or assistance capabilities, since this may intimidate users from adopting it. Using three analytical propositions, we further offer an optimal policy for smart device exploitation of private data for the purpose of interactions with users.

A Practical Privacy-Preserving Publishing Mechanism Based on Personalized k-Anonymity and Temporal Differential Privacy for Wearable IoT Applications

With the rapid development of the Internet of Things (IoT), wearable devices have become ubiquitous and interconnected in daily lives. Because wearable devices collect, transmit, and monitor humans’ physiological signals, data privacy should be a concern, as well as fully protected, throughout the whole process. However, the existing privacy protection methods are insufficient. In this paper, we propose a practical privacy-preserving mechanism for physiological signals collected by intelligent wearable devices. In the data acquisition and transmission stage, we employed existing asymmetry encryption-based methods. In the data publishing stage, we proposed a new model based on the combination and optimization of k-anonymity and differential privacy. An entropy-based personalized k-anonymity algorithm is proposed to improve the performance on processing the static and long-term data. Moreover, we use the symmetry of differential privacy and propose the temporal differential privacy mechanism for real-time data to suppress the privacy leakage while updating data. It is proved theoretically that the combination of the two algorithms is reasonable. Finally, we use smart bracelets as an example to verify the performance of our mechanism. The experiment results show that personalized k-anonymity improves up to 6.25% in terms of security index compared with traditional k-anonymity, and the grouping results are more centralized. Moreover, temporal differential privacy effectively reduces the amount of information exposed, which protects the privacy of IoT-based users.

Introducing the Privacy Aspect to Systems Thinking Assessment Method

Systems thinking is a valuable skill that may be required for an individual to be promoted in the business arena to managerial or leading positions. Thus, assessing systems thinking skills is an essential transaction for decision makers in the organization as a preceding step to the promotion decision. One of the well-known and validated tools for this task is a questionnaire. However, because some of the questions invade the employee or candidate’s privacy, the answer may be biased. In this paper, we consider this potential bias, a phenomenon that is becoming more and more significant as privacy concerns and awareness continuously increase in the modern digital world. We propose a formal methodology to optimize the questionnaire based on the privacy sensitivity of each question, thereby providing a more reliable assessment. We conducted an empirical study (n=142) and showed that a systems skills questionnaire can be enhanced. This research makes a significant contribution to improving the systems skills assessment process in particular, and lays the foundations for improving the evaluation of other skills or traits.

Utilizing IoT to design a relief supply chain network for the SARS-COV-2 pandemic

The current universally challenging SARS-COV-2 pandemic has transcended all the social, logical, economic, and mortal boundaries regarding global operations. Although myriad global societies tried to address this issue, most of the employed efforts seem superficial and failed to deal with the problem, especially in the healthcare sector. On the other hand, the Internet of Things (IoT) has enabled healthcare system for both better understanding of the patient's condition and appropriate monitoring in a remote fashion. However, there has always been a gap for utilizing this approach on the healthcare system especially in agitated condition of the pandemics. Therefore, in this study, we develop two innovative approaches to design a relief supply chain network is by using IoT to address multiple suspected cases during a pandemic like the SARS-COV-2 outbreak. The first approach (prioritizing approach) minimizes the maximum ambulances response time, while the second approach (allocating approach) minimizes the total critical response time. Each approach is validated and investigated utilizing several test problems and a real case in Iran as well. A set of efficient meta-heuristics and hybrid ones is developed to optimize the proposed models. The proposed approaches have shown their versatility in various harsh SARS-COV-2 pandemic situations being dealt with by managers. Finally, we compare the two proposed approaches in terms of response time and route optimization using a real case study in Iran. Implementing the proposed IoT-based methodology in three consecutive weeks, the results showed 35.54% decrease in the number of confirmed cases.

Secure Patient Data Transfer Using Information Embedding and Hyperchaos

Health 4.0 is an extension of the Industry standard 4.0 which is aimed at the virtualization of health-care services. It employs core technologies and services for integrated management of electronic health records (EHRs), captured through various sensors. The EHR is processed and transmitted to distant experts for better diagnosis and improved healthcare delivery. However, for the successful implementation of Heath 4.0 many challenges do exist. One of the critical issues that needs attention is the security of EHRs in smart health systems. In this work, we have developed a new interpolation scheme capable of providing better quality cover media and supporting reversible EHR embedding. The scheme provides a double layer of security to the EHR by firstly using hyperchaos to encrypt the EHR. The encrypted EHR is reversibly embedded in the cover images produced by the proposed interpolation scheme. The proposed interpolation module has been found to provide better quality interpolated images. The proposed system provides an average peak signal to noise ratio (PSNR) of 52.38 dB for a high payload of 0.75 bits per pixel. In addition to embedding EHR, a fragile watermark (WM) is also encrypted using the hyperchaos embedded into the cover image for tamper detection and authentication of the received EHR. Experimental investigations reveal that our scheme provides improved performance for high contrast medical images (MI) when compared to various techniques for evaluation parameters like imperceptibility, reversibility, payload, and computational complexity. Given the attributes of the scheme, it can be used for enhancing the security of EHR in health 4.0.

Enabling Clustering for Privacy-Aware Data Dissemination Based on Medical Healthcare-IoTs (MH-IoTs) for Wireless Body Area Network

There is a need to develop an effective data preservation scheme with minimal information loss when the patient's data are shared in public interest for different research activities. Prior studies have devised different approaches for data preservation in healthcare domains; however, there is still room for improvement in the design of an elegant data preservation approach. With that motivation behind, this study has proposed a medical healthcare-IoTs-based infrastructure with restricted access. The infrastructure comprises two algorithms. The first algorithm protects the sensitivity information of a patient with quantifying minimum information loss during the anonymization process. The algorithm has also designed the access polices comprising the public access, doctor access, and the nurse access, to access the sensitivity information of a patient based on the clustering concept. The second suggested algorithm is K-anonymity privacy preservation based on local coding, which is based on cell suppression. This algorithm utilizes a mapping method to classify the data into different regions in such a manner that the data of the same group are placed in the same region. The benefit of using local coding is to restrict third-party users, such as doctors and nurses, when trying to insert incorrect values in order to access real patient data. Efficiency of the proposed algorithm is evaluated against the state-of-the-art algorithm by performing extensive simulations. Simulation results demonstrate benefits of the proposed algorithms in terms of efficient cluster formation in minimum time, minimum information loss, and execution time for data dissemination.

Particulate Matter Monitoring and Assessment through Internet of Things: a Health Information System for Enhanced Living Environments

People spend most of their time inside buildings. Therefore, indoor air quality monitoring contributes to improve health and well-being. Several studies focus on the critical impact of particulate matter on residential air quality. In 2016, particulate matter caused 412 thousand premature deaths in 41 European countries. This paper presents the development of an affordable health information system for enhanced living environments. The authors propose a cost-effective, modular, scalable, and easy installation solution for particulate matter monitoring. The system is connected to ThingSpeak. It can be installed in any type of building. It requires only a power source and a Wi-Fi network with internet access. The main contribution of this paper is to present the detailed architecture and testing results. The particulate matter monitoring system was installed for one week in a domestic kitchen with an open fireplace. The results showed impact of the biomass burning on indoor air quality. The mean values per day ranged from: 10.53 to 50.62 μg/m³ for PM_1.0, 15.35 to 69.37 μg/m³ for PM_2.5, and 20.1 to 90.69 μg/m³ for PM₁₀. The maximum values per hour were registered at 13:00: 72.14 μg/m³ for PM_1.0, 99.70 μg/m³ for PM_2.5, and 132.13 μg/m³ for PM₁₀. Cost-effective sensors do not have the accuracy level of industrial equipment. Therefore, they should not be used for numerical and in-depth accurate characterization of the environment. Nevertheless, continuous particulate matter monitoring provides consistent data series for analysis of indoor air quality evolution.

Deep Learning in Biometrics: A Survey

Deep learning has been established in the last few years as the gold standard for data processing, achieving peak performance in image, text and audio understanding. At the same time, digital security is of utmost importance in this day and age, where everyone could get into our personal devices like cellphones or laptops, where we store our most valuable information. One of the possible ways to prevent this is via advanced and personalized security: biometrics. In this survey, it is considered how the scientific advances in the field of deep learning are applied to biometrics in order to enhance the protection of our data.