Healthcare Security Research Articles

Modeling security evaluation framework for IoHT-driven systems using integrated decision-making methodology

The intensification of the Internet of Health Things devices created security concerns due to the limitations of these devices and the nature of the healthcare data. While dealing with the security challenges, several authentication schemes, protocols, processes, and standards have been adopted. Consequently, making the right decision regarding the installation of a secure authentication solution or procedure becomes tricky and challenging due to the large number of security protocols, complexity, and lack of understanding. The major objective of this study is to propose an IoHT-based assessment framework for evaluating and prioritizing authentication schemes in the healthcare domain. Initially, in the proposed work, the security issues related to authentication are collected from the literature and consulting experts’ groups. In the second step, features of various authentication schemes are collected under the supervision of an Internet of Things security expert using the Delphi approach. The collected features are used to design suitable criteria for assessment and then Graph Theory and Matrix approach applies for the evaluation of authentication alternatives. Finally, the proposed framework is tested and validated to ensure the results are consistent and accurate by using other multi-criteria decision-making methods. The framework produces promising results such as 93%, 94%, and 95% for precision, accuracy, and recall, respectively in comparison to the existing approaches in this area. The proposed framework can be picked as a guideline by healthcare security experts and stakeholders for the evaluation and decision-making related to authentication issues in IoHT systems

Decentralized Blockchain Solutions for Enhanced Document Security in Healthcare

Decentralized Blockchain Solutions for Enhanced Document Security in Healthcare

Revolutionizing Identity Verification and Recognition

The innovative integration of real-time human detection improves both security and user experience in identity authentication. This approach improves authentication processes by analysing physiological and behavioural indicators such as face movements and eye movements, hence reducing fraud risks. Its deployment across sectors promises to enhance security in finance, healthcare, e-commerce, and law enforcement. Despite the hurdles, the benefits of this human-centered approach are enormous, indicating a more secure digital future. Keywords— Identity verification, live human detection, digital security, biometric authentication, user experience, fraud prevention, computer vision, physiological and behavioural cues, facial and vocal patterns, finance, healthcare, e-commerce, law enforcement.

Non-governmental organizations’ development interventions and community empowerment in Kitui County, Kenya

Non-governmental organizations (NGOs) are crucial actors in international development, addressing global challenges and fostering sustainable development in communities worldwide. Despite concerted NGO efforts, Kitui County in Eastern Kenya remains underdeveloped, grappling with significant challenges in education, healthcare, women empowerment, and food security. With 48% of its population living in poverty, surpassing the national average, and 39.4% facing food insecurity, Kitui County is in urgent need of intervention. This study investigates the impact of NGO interventions on community empowerment in Kitui County, focusing on education, health, and women empowerment sectors. Drawing on the Empowerment Theory and Sustainable Development Theory, a pragmatic research approach was adopted, employing a descriptive survey design. Data were collected from 384 residents aged 18 and above, utilizing structured questionnaires and interviews. Quantitative data were analyzed using the Statistical Package for the Social Sciences (SPSS), while qualitative data underwent thematic analysis. Findings reveal that NGO interventions have positively influenced education, with 66% acknowledging improved access to quality education through provided materials and teacher training programs. In healthcare, immunization campaigns (65%) and maternal and child health clinics (65%) have significantly reduced mortality rates and increased health awareness. Women empowerment initiatives, including microfinance and leadership training, have empowered women economically (64%) and facilitated their advocacy for rights (69%). This study underscores the vital role of NGOs in enhancing community empowerment in Kitui County. It emphasizes the need for sustained NGO interventions tailored to local contexts to address multifaceted development challenges effectively.

The need for cybersecurity self-evaluation in healthcare

The Australian healthcare sector is a complex mix of government departments, associations, providers, professionals, and consumers. Cybersecurity attacks, which have recently increased, challenge the sector in many ways; however, the best approaches for the sector to manage the threat are unclear. This study will report on a semi-structured focus group conducted with five representatives from the Australian healthcare and computer security sectors. An analysis of this focus group transcript yielded four themes: 1) the challenge of securing the Australian healthcare landscape; 2) the financial challenges of cybersecurity in healthcare; 3) balancing privacy and transparency; 4) education and regulation. The results indicate the need for sector-specific tools to empower the healthcare sector to mitigate cybersecurity threats, most notably using a self-evaluation tool so stakeholders can proactively prepare for incidents. Despite the vast amount of research into cybersecurity, little has been conducted on proactive cybersecurity approaches where security weaknesses are identified weaknesses before they occur.

Analysis of healthcare data security with DWT-HD-SVD based-algorithm invisible watermarking against multi-size watermarks

In the modern day, multimedia and digital resources play a crucial role in demystifying complex topics and improving communication. Additionally, images, videos, and documents speed data administration, fostering both individual and organizational efficiency. Healthcare providers use tools like X-rays, MRIs, and CT scans to improve diagnostic and therapeutic capacities, highlighting the importance of these tools in contemporary communication, data processing, and healthcare. Protecting medical data becomes essential for maintaining patient confidentiality and service dependability in a time when digital assets are crucial to the healthcare industry. In order to overcome this issue, this study analyses the DWT-HD-SVD algorithm-based invisible watermarking in medical data. The main goal is to verify medical data by looking at a DWT-based hybrid technique used on X-ray images with various watermark sizes (256*256, 128*128, 64*64). The algorithm’s imperceptibility and robustness are examined using metrics like Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) and are analyzed using Normalized Connection (NC), Bit Error Rate (BER), and Bit Error Rate (BCR) in order to evaluate its resistance to various attacks. The results show that the method works better with smaller watermark sizes than it does with larger ones.

IoT-BASED AUTOMATED HEALTH CARE MONITORING SYSTEM FOR SMART CITY

The Internet of Things (IoT) is essential in innovative applications such as smart cities, smart homes, education, healthcare, transportation, and defense operations. IoTapplications are particularly beneficial for providing healthcare because they enable secure and real-time remote patient monitoring to improve the quality of people’s lives. We provide a systematic review on recent studies of IoT-based healthcare-monitoring systems through literature review. The literature review compares various systems’ effectiveness, efficiency, data protection, privacy, security, and monitoring. The paper also explores wireless- and wearable-sensor-based IoT monitoring systems and provides a classification of healthcare-monitoring sensors. We also elaborate, in detail, on the challenges and open issues regarding healthcare security and privacy, and QoS. Finally, suggestions and recommendations for IoT healthcare applications are laid down at the end of the study along with future directions related to various recent technology trends. Keywords: IoT; IoT; healthcare; monitoring; remote.

Health Insurance Utilization and Financial Behaviour: A Study of Rashtriya Swasthya Bima Yojana (RSBY) in Kerala

This paper examines the health behaviour and health insurance landscape in Kerala, India, with a focus on the impact of the Rashtriya Swasthya Bima Yojana (RSBY) on healthcare utilization and financial behaviour. Using primary data collected from Mukkam municipality in Kozhikode district, Kerala, the study analyses the demographic profile of respondents, their health insurance status, and satisfaction levels with the RSBY scheme. The findings indicate a fairly balanced preference between government and private insurance schemes among households, with a significant majority possessing an insurance card. The RSBY scheme has been successful in enrolling a large proportion of households and providing satisfactory coverage at both government and private hospitals. However, there are variations in insurance coverage and satisfaction levels among different demographic subgroups, highlighting the need for targeted interventions and policy measures to improve access to healthcare and financial security among vulnerable populations.

Genetic Algorithm based Secure Hybrid Routing Technique for IoT Framework

Internet is gaining new heights in today’s technological environment. Internet of things (IoT) is a new class of Internet-basedheterogeneous networked application systems that uses various types of sensors/detectors and devices for the exchange and collection of data. Due to their applications in situations such as building home automation, emergencies, crisis management, energy management, and healthcare, path optimization and message security becomes of topmost importance in IoT. An optimized routing scheme using intelligent mathematical techniques including Genetic Algorithms (GA) and Analytical Hierarchy Process (AHP), is proposed here and an optimized route can be encrypted using cryptographic techniques. From the simulation results, it has been found that the overall efficiency of the IoT system can be greatly improved with the proposed model. A comparison is also provided in the discussion section which demonstrates that hybrid algorithms developed for IoT systems perform much better than traditional routing algorithms.

Multiple Disease Prediction System Using ML

Abstract: The increasing prevalence of diverse diseases presents a challenge to global healthcare systems, underscoring the need for innovative and efficient methods for early detection and preventive measures. This paper explores the application of machine learning algorithms in multiple disease prediction to enhance diagnostic accuracy and enable timely intervention. Leveraging diverse health-related data sources, including medical records and genomic information, comprehensive predictive models are developed. A multi-faceted machine learning approach integrates support vector machines, decision trees, neural networks, and ensemble learning methods to analyze complex data patterns. Feature selection and dimensionality reduction techniques optimize model performance and interpretability. The development of a predictive system involves essential steps such as data collection, preprocessing, and model training, followed by evaluation using metrics like accuracy and recall. Integration of Flask for web application development facilitates user interaction and prediction functionality. Deployment, testing, debugging, and ongoing maintenance ensure system efficiency and compliance with regulatory requirements for healthcare data security and privacy.

A Secure Healthcare Monitoring System for Disease Diagnosis in the IoT Environment

A Secure Healthcare Monitoring System for Disease Diagnosis in the IoT Environment

A Comprehensive Survey on the Data-Driven Approaches used for Tackling the COVID-19 Pandemic

The current evolution of Artificial Intelligence (AI) is fueled by the massive data sources generated by the Internet of Things (IoT), social media, and a diverse range of mobile and web applications. Machine learning (ML) and deep learning become the key to analyzing these data intelligently and developing complementary intelligent data-driven services in the healthcare sector. The world witnessed many AI-enabled tools that contributed to fighting against the COVID-19 pandemic and accelerated with unprecedented accuracy the development and the deployment of many countermeasures. The main objective of this study is to provide a comprehensive survey on the role of AI and ML methods in the healthcare sector. The study offers cases on how AI/ML can arm the world against future pandemics. Specifically, the study presents all available datasets, the main research problems related to COVID-19, and the solutions that AI and ML technologies offer. Finally, based on the analysis of the current literature, the limitations and open research challenges are highlighted. Our findings show that AI and ML technologies can play an essential role in COVID-19 forecasting, prediction, diagnosis, and analysis. In comparison, most of the previous works did not deploy a comprehensive framework that integrates the ML and DL with network security. This work emphasizes the mandate of including network security in all COVID-19 applications and providing complete and secure healthcare services.

A Comprehensive Discourse on Shallow Learning and its Applications

Shallow learning, a fundamental approach in machine learning, encompasses a variety of algorithms and techniques aimed at learning patterns and making predictions from labelled data. Unlike deep learning, which involves complex architectures with multiple layers of abstraction, shallow learning focuses on simpler models with limited complexity. This abstract explores the essence of shallow learning, its algorithms, applications, and challenges. Shallow learning algorithms include classic methods such as decision trees, support vector machines, k-nearest neighbours, and logistic regression, among others. These algorithms are typically trained using supervised learning techniques, where the model learns from input-output pairs to make predictions on new, unseen data. Shallow learning models excel in tasks such as classification and regression, where the goal is to assign labels or predict continuous values to input data. Applications of shallow learning span across various domains, including healthcare, finance, marketing, and cyber security. In healthcare, shallow learning models are used for disease diagnosis and prognosis prediction based on patient data. In finance, these models aid in fraud detection, credit scoring, and stock market prediction. Marketing applications involve customer segmentation and churn prediction, while in cyber security; shallow learning is utilized for malware detection and network intrusion detection.

Big data security and privacy in healthcare: A systematic review and future research directions

Big data security involves protecting healthcare data from unauthorised use and access and making any changes, while privacy entails ensuring the confidentiality of individual patient information within the datasets. The two are crucial in ensuring that big data is effectively applied for research and personalised care, compromising the sensitivity of healthcare data and medical information. Consequently, as the amount of data in the healthcare sector grows exponentially, securing sensitive data becomes increasingly crucial while effectively utilising big data analytics. The systematic review explores the issues and challenges associated with big data security and privacy in healthcare. Through reference to resource-based view theory, this paper seeks to examine the present state of research in this area, identify gaps in the existing literature, and propose strategies for future research. The search strategy looked at the data between 2013 and 2022 and yielded 93 studies from four databases: Scopus, PubMed, ScienceDirect and IEEE Xplore. From these studies, 18 papers are reviewed. The findings reveal that while significant progress has been made in securing healthcare data, numerous challenges still need to be considered. Overall, this review highlights the importance of continued research to ascertain that sensitive healthcare data is kept secure while allowing for the enhanced application of big data analytics. It contributes to the security and privacy problems associated with using big data in healthcare, measures for addressing these challenges, the healthcare value of big data, and foci for future research.

A Study on the Spatio-Temporal Evolution Characteristics of Social Development Levels in China

With the increase in regional economic development disparities, a regional coordinated development strategy is put forward that prioritizes human welfare and holistic social progress over a purely materialistic growth model. To address the challenges of balanced regional development, this paper has developed a multidimensional assessment framework of social development encompassing education, healthcare, culture, and social security. Using the entropy weight TOPSIS method, this study measures the social development level across 296 Chinese prefecture-level cities from 1990 to 2020. It explores the spatio-temporal evolution characteristics of China’s social development level through the Dagum Gini coefficient decomposition method and exploratory spatial data analysis. The results indicate that (1) the level of social development in China exhibits a fluctuating upward trend over the time series, showing a phase-wise pattern of decline–rise–rise; (2) there is a clear heterogeneity in the level of social development, with a general hierarchy of Eastern, Northeastern, Western, and Central regions in terms of social development; (3) spatially, China’s social development level has evolved from a patchy distribution in 1990 to a clustered distribution around urban agglomerations by 2020, with pronounced characteristics of spatial imbalance; (4) the level of social development in China displays varying degrees of spatial clustering, with this trend intensifying over time; and (5) over the period 1990–2020, the overall disparity in China’s social development level presents a fluctuating trend, with a notable reduction after an initial increase, and regional disparities following the order of Central, Western, Eastern, and Northeastern regions. This research offers valuable insights for policymakers and scholars seeking to understand and enhance China’s social development landscape.

SoK: Analyzing Privacy and Security of Healthcare Data from the User Perspective

Interactions in healthcare, by necessity, involve sharing sensitive information to achieve high-quality patient outcomes. Therefore, sensitive data must be carefully protected. This article explores existing privacy and security research conducted in the context of healthcare organizations. We conducted a systematic literature review of N =1,553 articles that examine the security and privacy of healthcare data and focus on 80 articles addressing human factors. Key findings show that much of the healthcare security and privacy research is focused on technology (44.11%, 712 articles), with a lack of emphasis on the human element (4.96%, 80 articles). In the subset of user studies, we find that patients and the general public express concerns about privacy and security with technologies like electronic health records (EHRs). Furthermore, our analysis shows that healthcare professionals often have low awareness of risks related to data security. Additionally, our analysis revealed that most research focuses narrowly on large hospitals, neglecting private practices and the unique challenges they face. We conclude by identifying research gaps and providing potential solutions to enable robust data security for sensitive patient data.

Enhancing health-care security: The role of blockchain and consensus mechanisms

Blockchain technology has gained prominence recently by virtue of its strong security features for clinical data. Automation of blockchain transactions enables data transactions and records, providing decentralized, secure, and dependable access. Through intelligence-sharing agreements, it can also manage member relationships without the need for a middleman or other third party. Researchers in the health-care industry using blockchain algorithms to safeguard security of data, which is properly stored, are on the rise. In addition, this technology is patient adaptive. Patients and other health-care users can now trust the technology because it prevents any third party from accessing the medical data. Many platforms intended for use in the health-care domain are emerging, including Gem Health Network and MedRec. Using blockchain in health-care protects user data and grants them full authority over their data. However, blockchain technology is also confronting challenges and limitations regarding data privacy and storage capacity. This paper explores the implementation of blockchain within health-care sector while providing an overview of this technology and the different consensus algorithms used in blockchain technology.

DID-HVC-based Web3 healthcare data security and privacy protection scheme

In response to the current situation that Web2 Internet medical giants monopolize data elements and deprive medical users of their data rights and interests, self-sovereign identity(SSI) in the open environment of “Web3+Medicine” will make it possible for users to control and manage their e-medical data independently. However, SSI is a new identity management paradigm, and in the face of frequent interactions between identity credentials and application services, the existing research schemes have a coarse granularity of identity privacy protection, so there is an urgent need for attestation methods that selectively minimize the disclosure of identity attributes. In this study, we propose a SSI attribute selectively disclosable scheme based on a decentralized identifiers-hash verifiable credentials (DID-HVC) and design a model, that enables medical users not only to selectively minimize the disclosure of identity attribute data using verifiable credentials (VCs) with hash claims and combines with DID incorporating random obfuscation factors, to prevent overexposure of personal and healthcare privacy information, but also to safeguard users’ VCs are not linked and analysed. Finally, we provide a security and practicality analysis of our scheme and do simulation experiments based on Ethernet. The experimental results show that our scheme can more securely solve the problem of excessive privacy leakage of medical users within a feasible timeframe.

Enhancing Healthcare Data Security Using Quantum Cryptography for Efficient and Robust Encryption

Healthcare requires data encryption to safeguard sensitive patient information from unauthorized access or breaches. Encryption ensures that data is encoded into a secure format, making it unreadable to anyone without the decryption key. This helps maintain patient privacy, protects against identity theft, and ensures compliance with data protection regulations like HIPAA (Health Insurance Portability and Accountability Act.). With encryption, healthcare organizations can mitigate the risk of data breaches and uphold trust with patients by ensuring the confidentiality and integrity of their medical records and personal information. Quantum cryptography emerges as a revolutionary solution, offering unparalleled encryption for healthcare data. This process initiates with the meticulous crafting of quantum states to encapsulate healthcare data securely, ensuring precise encoding. Subsequently, quantum entanglement is established between sender and receiver, creating a secure communication channel resistant to interception attempts. Quantum key distribution then generates a secure key leveraging entangled quantum states, becoming the cornerstone of encryption. Quantum encryption shields healthcare data in an impenetrable veil of secrecy, leveraging the complexities of quantum mechanics to thwart unauthorized decryption attempts. The decryption process, facilitated by the shared secure key, unveils the encrypted healthcare data exclusively to authorized parties, preserving confidentiality with unparalleled precision. The result reveals quantum cryptography's superiority in healthcare data encryption. At 150-bit key length, quantum cryptography takes 352,237 milliseconds vs. AES's 310,285 milliseconds. For a 14 KB input, quantum cryptography requires 7 milliseconds compared to AES's 12 milliseconds. These metrics highlight quantum cryptography's efficiency gains and transformative potential in healthcare data security, promising both enhanced security and efficiency benefits.

Enhancing Healthcare Data Security Using Quantum Cryptography for Efficient and Robust Encryption

Healthcare requires data encryption to safeguard sensitive patient information from unauthorized access or breaches. Encryption ensures that data is encoded into a secure format, making it unreadable to anyone without the decryption key. This helps maintain patient privacy, protects against identity theft, and ensures compliance with data protection regulations like HIPAA (Health Insurance Portability and Accountability Act.). With encryption, healthcare organizations can mitigate the risk of data breaches and uphold trust with patients by ensuring the confidentiality and integrity of their medical records and personal information. Quantum cryptography emerges as a revolutionary solution, offering unparalleled encryption for healthcare data. This process initiates with the meticulous crafting of quantum states to encapsulate healthcare data securely, ensuring precise encoding. Subsequently, quantum entanglement is established between sender and receiver, creating a secure communication channel resistant to interception attempts. Quantum key distribution then generates a secure key leveraging entangled quantum states, becoming the cornerstone of encryption. Quantum encryption shields healthcare data in an impenetrable veil of secrecy, leveraging the complexities of quantum mechanics to thwart unauthorized decryption attempts. The decryption process, facilitated by the shared secure key, unveils the encrypted healthcare data exclusively to authorized parties, preserving confidentiality with unparalleled precision. The result reveals quantum cryptography's superiority in healthcare data encryption. At 150-bit key length, quantum cryptography takes 352,237 milliseconds vs. AES's 310,285 milliseconds. For a 14 KB input, quantum cryptography requires 7 milliseconds compared to AES's 12 milliseconds. These metrics highlight quantum cryptography's efficiency gains and transformative potential in healthcare data security, promising both enhanced security and efficiency benefits.