Biblio

Intelligent Systems for Personal Data Cyber Security is a critical component of the Personal Information Management of Medicaid Enterprises. Intelligent Systems for Personal Data Cyber Security combines components of Cyber Security Systems with Human-Computer Interaction. It also uses the technology and principles applied to the Internet of Things. The use of software-hardware concepts and solutions presented in this report is, in the authors’ opinion, some step in the working-out of the Intelligent Systems for Personal Data Cyber Security in Medicaid Enterprises. These concepts may also be useful for developers of these types of systems.

Nowadays, 5G has been widely used in various fields. People are starting to turn their attention to 6G. Therefore, at the beginning, this paper describes in detail the principle and performance of 6G, and introduces the key technologies of 6G, Cavity technology and THz technology. Based on the high-performance indicators of 6G, we then study the possible application changes brought by 6G, for example, 6G technology will make remote surgery and remote control possible. 6G technology will make remote surgery and remote control possible. 6G will speed up the interconnection of everything, allowing closer and faster connection between cars. Next, virtual reality is discussed. 6G technology will enable better development of virtual reality technology and enhance people's immersive experience. Finally, we present the issues that need to be addressed with 6G technology, such as cybersecurity issues and energy requirements. As well as the higher challenges facing 6G technology, such as connectivity and communication on a larger social plane.

On the one hand, laparoscopic surgery as medical state-of-the-art method is minimal invasive, and thus less stressful for patients. On the other hand, laparoscopy implies higher demands on physicians, such as mental load or preparation time, hence appropriate technical support is essential for quality and suc-cess. Medical Digital Twins provide an integrated and virtual representation of patients' and organs' data, and thus a generic concept to make complex information accessible by surgeons. In this way, minimal invasive surgery could be improved significantly, but requires also a much more complex software system to achieve the various resulting requirements. The biggest challenges for these systems are the safe and precise mapping of the digital twin to reality, i.e. dealing with deformations, movement and distortions, as well as balance out the competing requirement for intuitive and immersive user access and security. The case study ARAILIS is presented as a proof in concept for such a system and provides a starting point for further research. Based on the insights delivered by this prototype, a vision for future Medical Digital Twins in surgery is derived and discussed.

Digitalization has occurred in almost all industries, one of them is health industry. Patients” medical records are now easier to be accessed and managed as all related data are stored in data storages or repositories. However, this system is still under development as number of patients still increasing. Lack of standardization might lead to patients losing their right to control their own data. Therefore, implementing private blockchain system with Self-Sovereign Identity (SSI) concept for identity management in health industry is a viable notion. With SSI, the patients will be benefited from having control over their own medical records and stored with higher security protocol. While healthcare providers will benefit in Know You Customer (KYC) process, if they handle new patients, who move from other healthcare providers. It will eliminate and shorten the process of updating patients' medical records from previous healthcare providers. Therefore, we suggest several flows in implementing blockchain for digital identity in healthcare industry to help overcome lack of patient's data control and KYC in current system. Nevertheless, implementing blockchain on health industry requires full attention from surrounding system and stakeholders to be realized.

Internet of Things devices collect and share data (IoT). Internet connections and emerging technologies like IoT offer privacy and security challenges, and this trend is anticipated to develop quickly. Internet of Things intrusions are everywhere. Businesses are investing more to detect these threats. Institutes choose accurate testing and verification procedures. In recent years, IoT utilisation has increasingly risen in healthcare. Where IoT applications gained popular among technologists. IoT devices' energy limits and scalability raise privacy and security problems. Experts struggle to make IoT devices more safe and private. This paper provides a machine-learning-based IDS for IoT network threats (ML-IDS). This study aims to implement ML-supervised IDS for IoT. We're going with a centralised, lightweight IDS. Here, we compare seven popular categorization techniques on three data sets. The decision tree algorithm shows the best intrusion detection results.

Attribute Based Encryption that solely decrypts the cipher text's secret key attribute. Patient information is maintained on trusted third party servers in medical applications. Before sending health records to other third party servers, it is essential to protect them. Even if data are encrypted, there is always a danger of privacy violation. Scalability problems, access flexibility, and account revocation are the main security challenges. In this study, individual patient health records are encrypted utilizing a multi-authority ABE method that permits a multiple number of authorities to govern the attributes. A strong key generation approach in the classic Attribute Based Encryption is proposed in this work, which assures the robust protection of health records while also demonstrating its effectiveness. Simulation is done by using CloudSim Simulator and Statistical reports were generated using Cloud Reports. Efficiency, computation time and security of our proposed scheme are evaluated. The simulation results reveal that the proposed key generation technique is more secure and scalable.

This paper analyzes techniques to enable differential privacy by adding Laplace noise to healthcare data. First, as healthcare data contain natural constraints for data to take only integral values, we show that drawing only integral values does not provide differential privacy. In contrast, rounding randomly drawn values to the nearest integer provides differential privacy. Second, when a variable is constructed using two other variables, noise must be added to only one of them. Third, if the constructed variable is a fraction, then noise must be added to its constituent private variables, and not to the fraction directly. Fourth, the accuracy of analytics following noise addition increases with the privacy budget, ϵ, and the variance of the independent variable. Finally, the accuracy of analytics following noise addition increases disproportionately with an increase in the privacy budget when the variance of the independent variable is greater. Using actual healthcare data, we provide evidence supporting the two predictions on the accuracy of data analytics. Crucially, to enable accuracy of data analytics with differential privacy, we derive a relationship to extract the slope parameter in the original dataset using the slope parameter in the noisy dataset.

Healthcare sectors such as hospitals, nursing homes, medical offices, and hospice homes encountered several obstacles due to the outbreak of Covid-19. Wearing a mask, social distancing and sanitization are some of the most effective methods that have been proven to be essential to minimize the virus spread. Lately, medical executives have been appointed to monitor the virus spread and encourage the individuals to follow cautious instructions that have been provided to them. To solve the aforementioned challenges, this research study proposes an autonomous medical assistance robot. The proposed autonomous robot is completely service-based, which helps to monitor whether or not people are wearing a mask while entering any health care facility and sanitizes the people after sending a warning to wear a mask by using the image processing and computer vision technique. The robot not only monitors but also promotes social distancing by giving precautionary warnings to the people in healthcare facilities. The robot can assist the health care officials carrying the necessities of the patent while following them for maintaining a touchless environment. With thorough simulative testing and experiments, results have been finally validated.

Data is a collection of information from the activities of the real world. The file in which such data is stored after transforming into a form that machines can process is generally known as data set. In the real world, many data sets are not complete, and they contain various types of noise. Missing values is of one such kind. Thus, imputing data of these missing values is one of the significant task of data pre-processing. This paper deals with two real time health care data sets namely life expectancy (LE) dataset and chronic kidney disease (CKD) dataset, which are very different in their nature. This paper provides insights on various data imputation techniques to fill missing values by analyzing them. When coming to Data imputation, it is very common to impute the missing values with measure of central tendencies like mean, median, mode Which can represent the central value of distribution but choosing the apt choice is real challenge. In accordance with best of our knowledge this is the first and foremost paper which provides the complete analysis of impact of basic data imputation techniques on various data distributions which can be classified based on the size of data set, number of missing values, type of data (categorical/numerical), etc. This paper compared and analyzed the original data distribution with the data distribution after each imputation in terms of their skewness, outliers and by various descriptive statistic parameters.

Considering the evolution of technology, the need to secure data is growing fast. When we turn our attention to the healthcare field, securing data and assuring privacy are critical conditions that must be accomplished. The information is sensitive and confidential, and the exchange rate is very fast. Over the years, the healthcare domain has gradually seen a growth of interest regarding the interconnectivity of different processes to optimize and improve the services that are provided. Therefore, we need intelligent complex systems that can collect and transport sensitive data in a secure way. These systems are called cyber-physical systems. In healthcare domain, these complex systems are named medical cyber physical systems. The paper presents a brief description of the above-mentioned intelligent systems. Then, we focus on wireless sensor networks and the issues and challenges that occur in securing sensitive data and what improvements we propose on this subject. In this paper we tried to provide a detailed overview about cyber-physical systems, medical cyber-physical systems, wireless sensor networks and the security issues that can appear.

The healthcare industry is confronted with a slew of significant challenges, including stringent regulations, privacy concerns, and rapidly rising costs. Many leaders and healthcare professionals are looking to new technology and informatics to expand more intelligent forms of healthcare delivery. Numerous technologies have advanced during the last few decades. Over the past few decades, pharmacy has changed and grown, concentrating less on drugs and more on patients. Pharmaceutical services improve healthcare's affordability and security. The primary invention was a cyber-infrastructure made up of smart gadgets that are connected to and communicate with one another. These cyber infrastructures have a number of problems, including privacy, trust, and security. These gadgets create cyber-physical systems for pharmaceutical care services in p-health. In the present period, cyber-physical systems for pharmaceutical care services are dealing with a variety of important concerns and demanding conditions, i.e., problems and obstacles that need be overcome to create a trustworthy and effective medical system. This essay offers a thorough examination of CPS's architectural difficulties and emerging tendencies.

Over the past two decades, several forms of non-intrusive technology have been deployed in cooperation with medical specialists in order to aid patients diagnosed with some form of mental, cognitive or psychological condition. Along with the availability and accessibility to applications offered by mobile devices, as well as the advancements in the field of Artificial Intelligence applications and Natural Language Processing, Conversational Agents have been developed with the objective of aiding medical specialists detecting those conditions in their early stages and monitoring their symptoms and effects on the cognitive state of the patient, as well as supporting the patient in their effort of mitigating those symptoms. Coupled with the recent advances in the the scientific field of machine and deep learning, we aim to explore the grade of applicability of such technologies into cognitive health support Conversational Agents, and their impact on the acceptability of such applications bytheir end users. Therefore, we conduct a systematic literature review, following a transparent and thorough process in order to search and analyze the bibliography of the past five years, focused on the implementation of Conversational Agents, supported by Artificial Intelligence technologies and in service of patients diagnosed with Mild Cognitive Impairment and its variants.

In recent years, business environments are undergoing disruptive changes across sectors [1]. Globalization and technological advances, such as artificial intelligence and the internet of things, have completely redesigned business activities, bringing to light an ever-increasing interest and attention towards the customer [2], especially in healthcare sector. In this context, researchers is paying more and more attention to the introduction of new technologies capable of meeting the patients’ needs [3, 4] and the Covid-19 pandemic has contributed and still contributes to accelerate this phenomenon [5]. Therefore, emerging technologies (i.e., AI-enabled solutions, service robots, conversational agents) are proving to be effective partners in improving medical care and quality of life [6]. Conversational agents, often identified in other ways as “chatbots”, are AI-enabled service robots based on the use of text [7] and capable of interpreting natural language and ensuring automation of responses by emulating human behavior [8, 9, 10]. Their introduction is linked to help institutions and doctors in the management of their patients [11, 12], at the same time maintaining the negligible incremental costs thanks to their virtual aspect [13–14]. However, while the utilization of these tools has significantly increased during the pandemic [15, 16, 17], it is unclear what benefits they bring to service delivery. In order to identify their contributions, there is a need to find out which activities can be supported by conversational agents.This paper takes a grounded approach [18] to achieve contextual understanding design and to effectively interpret the context and meanings related to conversational agents in healthcare interactions. The study context concerns six chatbots adopted in the healthcare sector through semi-structured interviews conducted in the health ecosystem. Secondary data relating to these tools under consideration are also used to complete the picture on them. Observation, interviewing and archival documents [19] could be used in qualitative research to make comparisons and obtain enriched results due to the opportunity to bridge the weaknesses of one source by compensating it with the strengths of others. Conversational agents automate customer interactions with smart meaningful interactions powered by Artificial Intelligence, making support, information provision and contextual understanding scalable. They help doctors to conduct the conversations that matter with their patients. In this context, conversational agents play a critical role in making relevant healthcare information accessible to the right stakeholders at the right time, defining an ever-present accessible solution for patients’ needs. In summary, conversational agents cannot replace the role of doctors but help them to manage patients. By conveying constant presence and fast information, they help doctors to build close relationships and trust with patients.

The spread of Internet of Things (IoT) devices in our homes, healthcare, industries etc. are more easily infiltrated than desktop computers have resulted in a surge in botnet attacks based on IoT devices, which may jeopardize the IoT security. Hence, there is a need to detect these attacks and mitigate the damage. Existing systems rely on supervised learning-based intrusion detection methods, which require a large labelled data set to achieve high accuracy. Botnets are onerous to detect because of stealthy command & control protocols and large amount of network traffic and hence obtaining a large labelled data set is also difficult. Due to unlabeled Network traffic, the supervised classification techniques may not be used directly to sort out the botnet that is responsible for the attack. To overcome this limitation, a semi-supervised Deep Learning (DL) approach is proposed which uses Semi-supervised GAN (SGAN) for IoT botnet detection on N-BaIoT dataset which contains "Bashlite" and "Mirai" attacks along with their sub attacks. The results have been compared with the state-of-the-art supervised solutions and found efficient in terms of better accuracy which is 99.89% in binary classification and 59% in multi classification on larger dataset, faster and reliable model for IoT Botnet detection.

In healthcare 4.0 ecosystems, authentication of healthcare information allows health stakeholders to be assured that data is originated from correct source. Recently, biometric based authentication is a preferred choice, but as the templates are stored on central servers, there are high chances of copying and generating fake biometrics. An adversary can forge the biometric pattern, and gain access to critical health systems. Thus, to address the limitation, the paper proposes a scheme, PHBio, where an encryption-based biometric system is designed prior before storing the template to the server. Once a user provides his biometrics, the authentication process does not decrypt the data, rather uses a homomorphic-enabled Paillier cryptosystem. The scheme presents the encryption and the comparison part which is based on euclidean distance (EUD) strategy between the user input and the stored template on the server. We consider the minimum distance, and compare the same with a predefined threshold distance value to confirm a biometric match, and authenticate the user. The scheme is compared against parameters like accuracy, false rejection rates (FARs), and execution time. The proposed results indicate the validity of the scheme in real-time health setups.

Cloud computing has become an integral part of medical big data. The cloud has the capability to store the large data volumes has attracted more attention. The integrity and privacy of patient data are some of the issues that cloud-based medical big data should be addressed. This research work introduces data integrity auditing scheme for cloud-based medical big data. This will help minimize the risk of unauthorized access to the data. Multiple copies of the data are stored to ensure that it can be recovered quickly in case of damage. This scheme can also be used to enable doctors to easily track the changes in patients' conditions through a data block. The simulation results proved the effectiveness of the proposed scheme.

With the advent of the era of Internet of Things (IoT), the increasing data volume leads to storage outsourcing as a new trend for enterprises and individuals. However, data breaches frequently occur, bringing significant challenges to the privacy protection of the outsourced data management system. There is an urgent need for efficient and secure data sharing schemes for the outsourced data management infrastructure, such as the cloud. Therefore, this paper designs a dual-server-based data sharing scheme with data privacy and high efficiency for the cloud, enabling the internal members to exchange their data efficiently and securely. Dual servers guarantee that none of the servers can get complete data independently by adopting secure two-party computation. In our proposed scheme, if the data is destroyed when sending it to the user, the data will not be restored. To prevent the malicious deletion, the data owner adds a random number to verify the identity during the uploading procedure. To ensure data security, the data is transmitted in ciphertext throughout the process by using searchable encryption. Finally, the black-box leakage analysis and theoretical performance evaluation demonstrate that our proposed data sharing scheme provides solid security and high efficiency in practice.

This paper explores high throughput architectures for the substitution modules, which are an integral component of encryption algorithms. The security algorithms chosen belong to the category of lightweight crypto-primitives suitable for pervasive computing. The focus of this work is on the implementation of encryption algorithms on hardware platforms to improve speed and facilitate optimization in the area and power consumption of the design. In this work, the architecture for the encryption algorithms' substitution box (S-box) is modified using switching circuits (i.e., MUX-based) along with a logic generator and included in the overall cipher design. The modified architectures exhibit high throughput and consume less energy in comparison to the state-of-the-art designs. The percentage increase in throughput or maximum frequency differs according to the chosen algorithms discussed elaborately in this paper. The evaluation of various metrics specific to the design are executed at RFID-specific frequency so that they can be deployed in an IoT environment. The designs are mainly simulated and compared on Nexys4 DDR FPGA platform, along with a few other FPGAs, to meet similar design and implementation environments for a fair comparison. The application of the proposed S-box modification is explored for the healthcare scenario with promising results.

While 5G Edge Computing along with IoT technology has transformed the future of healthcare data transmission, it presents security vulnerabilities and risks when transmitting patients' confidential information. Currently, there are very few reliable security solutions available for healthcare data that routes through SDN routers in 5G Edge Computing. These solutions do not provide cryptographic security from IoT sensor devices. In this paper, we studied how 5G edge computing integrated with IoT network helps healthcare data transmission for remote medical treatment, explored security risks associated with unsecured data transmission, and finally proposed a cryptographic end-to-end security solution initiated at IoT sensor devices and routed through SDN routers. Our proposed solution with cryptographic security initiated at IoT sensor goes through SDN control plane and data plane in 5G edge computing and provides an end-to-end secured communication from IoT device to doctor's office. A prototype built with two-layer encrypted communication has been lab tested with promising results. This analysis will help future security implementation for eHealth in 5G and beyond networks.

Blockchain and artificial intelligence are two technologies that, when combined, have the ability to help each other realize their full potential. Blockchains can guarantee the accessibility and consistent admittance to integrity safeguarded big data indexes from numerous areas, allowing AI systems to learn more effectively and thoroughly. Similarly, artificial intelligence (AI) can be used to offer new consensus processes, and hence new methods of engaging with Blockchains. When it comes to sensitive data, such as corporate, healthcare, and financial data, various security and privacy problems arise that must be properly evaluated. Interaction with Blockchains is vulnerable to data credibility checks, transactional data leakages, data protection rules compliance, on-chain data privacy, and malicious smart contracts. To solve these issues, new security and privacy-preserving technologies are being developed. AI-based blockchain data processing, either based on AI or used to defend AI-based blockchain data processing, is emerging to simplify the integration of these two cutting-edge technologies.

Unmanned autonomous vehicles (UAVs) have been receiving high interest lately due to their wide range of potential deployment options that can touch all aspects of our life and economy, such as transportation, delivery, healthcare, surveillance. However, UAVs have also introduced many new vulnerabilities and attack surfaces that can be exploited by cyberattacks. Due to their complexity, autonomous operations, and being relatively new technologies, cyberattacks can be persistent, complex, and can propagate rapidly to severely impact the main UAV functions such as mission management, support, processing operations, maneuver operations, situation awareness. Furthermore, such cyberattacks can also propagate among other UAVs or even their control stations and may even endanger human life. Hence, we need self-protection techniques with an autonomic management approach. In this paper we present our approach to implement self-protection of UAVs (SP-UAV) such that they can continue their critical functions despite cyberattacks targeting UAV operations or services. We present our design approach and implementation using a unified management interface based on three ports: Configuration, observer, and control ports. We have implemented the SP-UAV using C and demonstrated using different attack scenarios how we can apply autonomic responses without human involvement to tolerate cyberattacks against the UAV operations.

Wireless mesh networks are increasingly deployed as a flexible and low-cost alternative for providing wireless services for a variety of applications including community mesh networking, medical applications, and disaster ad hoc communications, sensor and IoT applications. However, challenges remain such as interference, contention, load imbalance, and congestion. To address these issues, previous work employ load adaptive routing based on load sensitive routing metrics. On the other hand, such approach does not immediately improve network performance because the load estimates used to choose routes are themselves affected by the resulting routing changes in a cyclical manner resulting to oscillation. Although this is not a new phenomenon and has been studied in wired networks, it has not been investigated extensively in wireless mesh and/or sensor networks. We present these instabilities and how they pose performance, security, and energy issues to these networks. Accordingly, we present a feedback-aware mapping system called FARM that handles these instabilities in a manner analogous to a control system with feedback control. Results show that FARM stabilizes routes that improves network performance in throughput, delay, energy efficiency, and security.

Data security has become an increasing concern with rampant data security regulation changes and the rampant deployment of technology. The necessity to lock down user data has never been greater. This research contributes to the secure software development of Android applications by identifying data processing concerns following the guidelines put forth by the Open Web Application Security Project “(OWASP) Mobile Top 10.” We found that 43.62% of the applications contained at least one security violation. We will be using an open source tool static analysis tool, MobSF, to review the security of 200 health related Android applications. The security of healthcare related applications should be given special attention, as they store and process highly sensitive information such as blood pressures, pulse rate, body photos, mental-state, OBGYN status, and sleep patterns. Partial automation techniques were utilized. This paper also suggests possible security remediations for the identified security concerns.

The problem of maintaining the privacy of sensitive healthcare data is crucial yet the significance of research efforts achieved still need robust development in privacy protection techniques for Wireless Multimedia Sensor Networks (WMSNs). This paper aims to investigate different privacy-preserving methods for WMSNs that can be applied in healthcare, to guarantee a privacy-aware transmission of multimedia data between sensors and base stations. The combination of ant colony optimization-based routing and hierarchical structure of the network have been proposed in the AntSensNet WMSN-based routing protocol to offer QoS and power efficient multipath multimedia packet scheduling. In this paper, the AntSensNet routing protocol was extended by utilizing privacy-preserving mechanisms thus achieving anonymity / pseudonymity, unlinkability, and location privacy. The vulnerability of standard AntSensNet routing protocol to privacy threats have raised the need for the following privacy attacks’ countermeasures: (i) injection of fake traffic, which achieved anonymity, privacy of source and base locations, as well as unlinkability; (ii) encrypting and correlating the size of scalar and multimedia data which is transmitted through a WMSN, along with encrypting and correlating the size of ants, to achieve unlinkability and location privacy; (iii) pseudonyms to achieve unlinkability. The impact of these countermeasures is assessed using quantitative performance analysis conducted through simulation to gauge the overhead of the added privacy countermeasures. It can be concluded that the introduced modifications did enhance the privacy but with a penalty of increased delay and multimedia jitter. The health condition of a patient determines the vitals to be monitored which affects the volumes and sources of fake traffic. Consequently, desired privacy level will dictate incurred overhead due to multimedia transmissions and privacy measures.

The protection of Intellectual Property (IP) has gradually increased in recent years. Traditional intellectual property management service has lower efficiency for such scale of data. Considering that the maturity of deep learning models has led to the development of knowledge graphs. Relevant researchers have investigated the application of knowledge graphs in different domains, such as medical services, social media, etc. However, few studies of knowledge graphs have been undertaken in the domain of intellectual property. In this paper, we introduce the process of building a domain knowledge graph and start from data preparation to conduct the research of named entity recognition.