Healthcare Security Research Articles

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.

An improved blockchain framework for ORAP verification and data security in healthcare

An improved blockchain framework for ORAP verification and data security in healthcare

Integration of Ethereum Blockchain with Cloud Computing for Secure Healthcare Data Management System

Effective management of health data is critical in this age of digital change. In order to solve the issues with safe healthcare data systems, this paper suggests a brand-new Hybrid Healthcare Data Management System (HDMS) that seamlessly combines blockchain and cloud computing technology. The solution guarantees the safe storage and effective administration of health data by utilizing the scalability and flexibility of cloud computing. The Ethereum blockchain, which offers immutability through smart contracts for data integrity assurance, is used to improve security. Important components of the suggested approach include Blockchain Anchoring, which uses distinct hash references on Ethereum to ensure data integrity, Google Cloud Integration for scalable storage and immediate data access, and compliance with Health Level 7 (HL7) formatting criteria for medical data storage. Robust privacy safeguards include cutting-edge methods like decentralized identifiers (DIDs) and homomorphic encryption, which guarantee secure computations on encrypted data and trustworthy identification for allowed access. IPFS file storage is used by the system to improve security by providing redundancy and resilience. By utilizing blockchain's immutability and cloud storage's distributed architecture, HDMS demonstrates resilience against disturbances. Results of the proposed method is implemented in Python Software. The Homomorphic approach that has been suggested continuously beats Optimized Blowfish Algorithm (OBA) in terms of encryption and decryption times. The improvement in encryption is between 3000ms (10 kb) and 1350ms (40 kb). The enhancement demonstrates improved efficiency across data sizes, ranging from 4000ms (10 kb) to 3350ms (40 kb) in decryption. The goal of HDMS is to create a future in which patient outcomes are enhanced by the appropriate handling, storage, analysis, and use of medical data.

IoT-based external attacks aware secure healthcare framework using blockchain and SB-RNN-NVS-FU techniques.

In recent times, there has been widespread deployment of Internet of Things (IoT) applications, particularly in the healthcare sector, where computations involving user-specific data are carried out on cloud servers. However, the network nodes in IoT healthcare are vulnerable to an increased level of security threats. This paper introduces a secure Electronic Health Record (EHR) framework with a focus on IoT. Initially, the IoT sensor nodes are designated as registered patients and undergo initialization. Subsequently, a trust evaluation is conducted, and the clustering of trusted nodes is achieved through the application of Tasmanian Devil Optimization (STD-TDO) utilizing the Student's T-Distribution. Utilizing the Transposition Cipher-Squared random number generator-based-Elliptic Curve Cryptography (TCS-ECC), the clustered nodes encrypt four types of sensed patient data. The resulting encrypted data undergoes hashing and is subsequently added to the blockchain. This configuration functions as a network, actively monitored to detect any external attacks. To accomplish this, a feature reputation score is calculated for the network's features. This score is then input into the Swish Beta activated-Recurrent Neural Network (SB-RNN) model to classify potential attacks. The latest transactions on the blockchain are scrutinized using the Neutrosophic Vague Set Fuzzy (NVS-Fu) algorithm to identify any double-spending attacks on non-compromised nodes. Finally, genuine nodes are granted permission to decrypt medical records. In the experimental analysis, the performance of the proposed methods was compared to existing models. The results demonstrated that the suggested approach significantly increased the security level to 98%, reduced attack detection time to 1300 ms, and maximized accuracy to 98%. Furthermore, a comprehensive comparative analysis affirmed the reliability of the proposed model across all metrics. The proposed healthcare framework's efficiency is proved by the experimental evaluation.

Elevating security and disease forecasting in smart healthcare through artificial neural synchronized federated learning

Elevating security and disease forecasting in smart healthcare through artificial neural synchronized federated learning

Strategic Assessment of Intricacies in Healthcare Cyber Security: Analyzing Distinctive Challenges, Evaluating Their Ramifications on Healthcare Delivery, and Proposing Advanced Mitigation Strategies

Healthcare firms have access to highly sensitive and valuable data, such as patient health records and payment card information. They are also becoming more reliant on Internet of Medical Things (IoMT) devices to provide treatment, and assaults on this networked equipment can result in data breaches or disruptions to crucial care of patients. Hence there is need to critically assess the intricacies in healthcare cyber security by analyzing the unique challenges facing healthcare sector, their impact on healthcare delivery and suggest some effective cyber security measures to mitigate the identified Cyber security challenges facing health care sector. The research employs a quantitative method by using the descriptive and survey approach through the use of questionnaires to elicit and gather relevant information regarding the Unique Cyber security Challenges in the Healthcare Industry. The descriptive method was used to achieve the objectives of the study while the survey technique was used to get qualitative information from respondents about effect of those cyber security challenges in the health care industry. The target sample size used for this study was 1300. However, only 980 responses were recovered and used for the analysis. Future research work suggested that strong encryption procedures can be developed and implemented for healthcare security, sophisticated anomaly detection tools can be incorporated, and cooperative frameworks for information exchange across the healthcare ecosystem could be established. Also the suggested effective measures to mitigate cyber security challenges in healthcare sector can be implemented in order to secure the patient sensitive and valuable data.

Enhancing Data Privacy in Wireless Sensor Networks: Investigating Techniques and Protocols to Protect Privacy of Data Transmitted Over Wireless Sensor Networks in Critical Applications of Healthcare and National Security

The article discusses the emergence of Wireless Sensor Networks (WSNs) as a groundbreaking technology in data processing and communication. It outlines how WSNs, composed of dispersed autonomous sensors, are utilized to monitor physical and environmental factors, transmitting data wirelessly for analysis. The article explores various applications of WSNs in healthcare, national security, emergency response, and infrastructure monitoring, highlighting their roles in enhancing patient care, public health surveillance, border security, disaster management, and military operations. Additionally, it examines the foundational concepts of data privacy in WSNs, focusing on encryption techniques, authentication mechanisms, anonymization techniques, and access control mechanisms. The article also addresses vulnerabilities, threats, and challenges related to data privacy in healthcare and national security contexts, emphasizing regulatory compliance, ethical considerations, and socio-economic factors. Furthermore, it introduces the Diffusion of Innovation Theory as a framework for understanding the adoption of privacy-enhancing technologies in WSNs. Finally, the article reviews empirical studies demonstrating the efficacy of security solutions in preserving data privacy in WSNs, offering insights into advancements in safeguarding sensitive information.

The Transformation Risk-Benefit Model of Artificial Intelligence: Balancing Risks and Benefits Through Practical Solutions and Use Cases

This paper summarizes the most cogent advantages and risks associated with Artificial Intelligence from an in-depth review of the literature. Then the authors synthesize the salient risk-related models currently being used in AI, technology and business-related scenarios. Next, in view of an updated context of AI along with theories and models reviewed and expanded constructs, the writers propose a new framework called “The Transformation Risk-Benefit Model of Artificial Intelligence” to address the increasing fears and levels of AIrisk. Using the model characteristics, the article emphasizes practical and innovative solutions where benefitsoutweigh risks and three use cases in healthcare, climate change/environment and cyber security to illustrate unique interplay of principles, dimensions and processes of this powerful AI transformational model.

Blockchain Solutions for Secure Healthcare Data Management: A Comprehensive Survey

The paper introduces an innovative approach to healthcare data security, addressing the critical concerns surrounding privacy and data integrity in the modern healthcare landscape. By amalgamating blockchain technology, AWS S3 services, and attribute-based access control (ABAC), the proposed solution offers a robust framework for safeguarding sensitive medical information. Through the implementation of ABAC, the solution enables dynamic and precise access control, ensuring that only authorized entities can access and manipulate healthcare data. Additionally, the utilization of a blockchain-based XML ledger on AWS S3 services enhances data security by providing an immutable and tamper-proof storage mechanism. This comprehensive solution not only addresses the privacy challenges inherent in the digitization of healthcare but also facilitates secure data sharing among authorized parties. By leveraging the combined strengths of ABAC, blockchain technology, and AWS S3 services, the proposed framework delivers a scalable and efficient infrastructure for managing healthcare data securely. It represents a significant advancement in healthcare information security, promising to safeguard patient privacy and maintain the accuracy and reliability of medical data in an increasingly interconnected digital ecosystem. Key Words: AWS S3 Service, Blockchain technology, medical information, XML ledger, Attribute-based access control (ABAC), Tamper-proof

The Safety and Effectiveness of Bevacizumab in Metastatic Colorectal Cancer With Unresectable Metastases: A Real-Life Study From the South of Morocco.

Background Colorectal cancer constitutes a significant public health challenge, despite remarkable strides made in the last two decades, particularly in the medical management of metastatic stages. Notable progress has been achieved through targeted therapies such as anti-epidermal growth factor receptors or anti-angiogenic antibodies, as well as advancements in surgical approaches for hepatic metastases. This study seeks to assess the efficacy and safety of bevacizumab plus chemotherapy in individuals with metastatic colorectal cancer. Methodology This is an observational, cross-sectional, retrospective study of all patients who were followed up for metastatic colorectal cancer with unresectable metastases and were treated with bevacizumab in combination with standard chemotherapy from January 2010 until December 2019 in the medical oncology department of the Centre Hospitalier Universitaire (CHU) Souss-Massa of Agadir. Results Of the total 162 cases, 117 (72%) had metastatic disease, and 45 (28%) progressed to metastatic disease after initial treatment. The median age of the patients was 55 years (range = 23-79 years) with a sex ratio of 1.1 (M/F). The tumor was located in the left colon in 135 (83.3%)patients. The results represented adenocarcinoma in 137 (84.6)cases and mucinous subtype in 23 (14.19) cases. The three most common sites of metastasis were the liver (99, 61.1), peritoneum (67, 41.3), and lung (33, 20.3). In the first line, allpatients received bi-chemotherapy plus bevacizumab, i.e., fluorouracil, oxaliplatin, and leucovorin in 34 (20.9%) patients; capecitabine plus oxaliplatin in 88 (54.3%) patients; leucovorin, fluorouracil, and irinotecan in 17 (10.4%) patients; andcapecitabine plusirinotecan in 23 (14.1%) patients. Response after first-line treatment was progression in 74 (45.7) cases, stability in 42 (25.9) cases, partial response in 35(21.6) cases, and complete response in 11 (6.8) cases. Nine (6%) patients were able to benefit from surgical resection of metastatic lesions. Overall, 77 (47%)patients received second-line chemotherapy, i.e., 5-FU with irinotecan in 40 (51.8%)cases or with oxaliplatin in 30(38.8%) cases. Two patients developed undesirable side effects under bevacizumab (hypertension). The median progression-free survival and median overall survival of the study cohort were 9 months and 14 months, respectively. Nevertheless, patients who underwent primary tumor resection (p = 0.048), those with right‑sided tumors (p = 0.022), those who received a higher number of treatment cycles (p = 0.020), and those who received maintenance treatment (p = 0.001) had a longer median overall survival. Conclusions Chemotherapy combination with bevacizumab is considered as the cornerstone of metastatic colorectal cancer treatment in our region. With the new healthcare and social security systems, easier access to expensive treatments and molecular pathology tests is currently available. It is important to highlight that real-world data can offer valuable insights into the daily clinical practice of medical oncology.

SHIS: secure healthcare intelligent scheme in internet of multimedia vehicular environment

SHIS: secure healthcare intelligent scheme in internet of multimedia vehicular environment

Adversarial Transferability in Embedded Sensor Systems: An Activity Recognition Perspective

Machine learning algorithms are increasingly used for inference and decision-making in embedded systems. Data from sensors are used to train machine learning models for various smart functions of embedded and cyber-physical systems ranging from applications in healthcare, autonomous vehicles, and national security. However, recent studies have shown that machine learning models can be fooled by adding adversarial noise to their inputs. The perturbed inputs are called adversarial examples. Furthermore, adversarial examples designed to fool one machine learning system are also often effective against another system. This property of adversarial examples is called adversarial transferability and has not been explored in wearable systems to date. In this work, we take the first stride in studying adversarial transferability in wearable sensor systems from four viewpoints: (1) transferability between machine learning models; (2) transferability across users/subjects of the embedded system; (3) transferability across sensor body locations; and (4) transferability across datasets used for model training. We present a set of carefully designed experiments to investigate these transferability scenarios. We also propose a threat model describing the interactions of an adversary with the source and target sensor systems in different transferability settings. In most cases, we found high untargeted transferability, whereas targeted transferability success scores varied from 0% to 80%. The transferability of adversarial examples depends on many factors such as the inclusion of data from all subjects, sensor body position, number of samples in the dataset, type of learning algorithm, and the distribution of source and target system dataset. The transferability of adversarial examples decreased sharply when the data distribution of the source and target system became more distinct. We also provide guidelines and suggestions for the community for designing robust sensor systems. Code and dataset used in our analysis is publicly available here. 1

An Overview on Prescription Error

An overview of prescription errors among patients is studied. A prescription error is defined as a therapeutic process failure that results in or may result in patient damage. Errors with prescriptions states that at least 1.5 million people are harmed annually by one of the most prevalent medical blunders. While there are many benefits of using medicines, as well as there are also risks, which might occur due to prescription errors. One of the most frequent ways that patients suffer is from improper prescription management, which is often the result of prescribing errors. The methods to lower prescription errors are examined in this evidence scan. Finding the cause of prescription errors and raising public awareness are its two main goals to reduce errors. Make errors or inconsistencies in the selection and administration of medications, such as incorrect dosages, wrong route of administration, and unnoticed drug interaction. Prescription errors are more likely to occur in older adults. Errors in prescription drugs raise health care expenses and reduces patient trust in the medical system. Managers and health experts are constantly searching for methods to raise the standard and guarantee the security of healthcare. This document discusses medication errors, which are instances of drug mis adventuring that should be avoided by implementing efficient system controls. These controls should involve pharmacists, doctors, nurses, risk management staff, attorneys, administrators, patients, and other members of the organizational setting in addition to regulatory bodies and the pharmaceutical industry

IoT based Health Monitoring System

The Internet of Things (IoT) is essential in innovative applications such as smart cities, smart homes, education, healthcare, transportation, and defense operations. IoT applications are particularly beneficial for providing healthcare because they enable secure and real- time remote patient monitoring to improve the quality of people’s lives. This review paper explores the latest trends in healthcare-monitoring systems by implementing the role of the IoT. The work discusses the benefits of IoT-based healthcare systems with regard to their significance, and the benefits of IoT healthcare. 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, IoWT, healthcare, monitoring, remote

A Comprehensive Survey on Blockchain Technology and Its Applications

This survey paper provides a comprehensive and in-depth overview of blockchain technology and its wide-ranging applications. It begins by introducing the fundamental characteristics and structure of blockchain, with a particular focus on the five major consensus mechanisms and their unique features. The article emphasizes the crucial role of smart contracts and cryptography in the construction and operation of blockchain networks. Furthermore, the paper explores the specific applications of blockchain in three key areas: cryptocurrencies, supply chains, and healthcare security. It highlights the numerous advantages that blockchain brings to these domains, including enhanced security, transparency, and efficiency. The paper also offers valuable insights into the future potential of blockchain technology in these areas, providing a glimpse into the possibilities that lie ahead. Additionally, the article addresses the challenges posed by the "impossible triangle" of decentralization, security, and high performance in blockchain. It discusses the emerging research trends aimed at tackling these challenges, such as cross-chain protocols, privacy protection mechanisms, blockchain expansion strategies, and advanced data storage solutions. The paper presents recent advancements and breakthroughs in each of these research directions, showcasing the ongoing efforts to overcome the limitations of blockchain technology.

Designing a Block Chain Based Network for the Secure Exchange of Medical Data in Healthcare Systems

ABSTRACT An identity-based cryptosystem called signcryption is investigated in this study in order to efficiently and securely exchange healthcare information inside a data-sharing network, utilizing the idea of bilinear pairings. A distributed tamperproof database protects all health records and is uploaded to it and replicated over a group of nodes that are linked to form a peer-to-peer network. As a result, each health record is treated as an individual event that is time stamped and assigned a cryptographic hash. On a similar note, transaction blocks include all the record events. Every node in the network has a copy of the ledger of hashed records and events. Additional information such as user permission lists are also included in this health care blockchain network, which serve as instructions for the network. To guarantee the data integrity of EHRs, this article provides security for exchanging EHRs across medical institutions and inside the organization, and ultimately safe exchange of EHRs by removing a third-party trustworthy third-party provider. Various cryptographic methods are being utilized on the blockchain in order to ensure security for the exchange of health care data. Signcryption is utilized in the suggested design approach for secure health care data exchange.

Legal union of a same-sex couple in the Slovak Republic and the scope of recognition of such unions validly established abroad

The paper focuses on the issue of legal regulation of unions of same-sex couples under the conditions of the Slovak legal order. The author notes that within the Slovak Republic there is no equivalent of the institution of marriage for cohabiting persons of the same sex. It is further specified on the issues of recognition of marriages of same-sex couples, respectively registered partnerships established abroad. The aim of the contribution is to summarize and stimulate a wider discussion on the issues addressed and to point out the fact that human rights must be guaranteed in a state of law, while their guarantee can be interpreted as a security granted to all and the rejection of discrimination. The lack of legal recognition for same-sex unions not only presents social challenges but also practical hindrances in daily affairs such as healthcare, taxation, and social security benefits, which can often be predicated on marital status. It is crucial to consider the implications of such legal gaps on the well-being of individuals who are part of same-sex couples, as well as their children. The evolving social fabric and changing attitudes towards same-sex relationships necessitate a reassessment of existing legal frameworks to ensure inclusivity and equality under the law. The continued efforts and discussions within the Slovak Republic highlight a growing awareness and potential for legislative change that aligns with the European consensus on human rights. Moreover, the exploration of legal avenues for recognition of same-sex unions must consider the dynamic nature of family structures in the contemporary context, where diversity in family units is becoming increasingly normative.

Cybersecurity Vulnerabilities in Healthcare: A Threat to Patient Security

Healthcare information systems hold significant importance; hence, their cybersecurity is crucial. Exposed networks make it easy for cybercriminals to launch cyberattacks and access healthcare data. Thus, concerns regarding cybersecurity and its link to healthcare privacy, confidential data, and medical devices are growing. Therefore, cybersecurity vulnerabilities in healthcare and patient security are significant issues. Healthcare information systems comprise correlated networks and play a vital role in treating and saving patients. However, advanced circulated ransomware attacks on hospitals prevent access to electronic health records for providing appropriate patient care, thus forcing doctors to turn to other facilities. These cyberattacks can leak patient data, and regaining control of information systems and patient data is highly expensive, thus resulting in extensive monetary losses. Cyberattacks aimed toward electronic medical records, information technology systems, and medical devices have corrupted the best systems across clinics and small offices with physicians, as well as merged health systems. There is an urgency to address cybersecurity vulnerabilities in healthcare; however, opinions differ regarding suitable measures for safeguarding patient data and ensuring infrastructural security. We aimed to reconcile these diverging hypotheses and provide an understanding of the current landscape and directions for further improvements by reviewing several studies on healthcare cybersecurity. We also interviewed healthcare professionals, cybersecurity experts, and administrators and distributed a survey questionnaire to healthcare organizations to gather quantitative data on existing cybersecurity measures and vulnerabilities. Our analyses show that healthcare organizations are vulnerable to a variety of threats, cyberattacks disrupt the health sector, cybersecurity vulnerabilities impact patient security, and implementations of cybersecurity measures are inconsistent across organizations. Owing to the sophisticated nature of cyberattacks, the healthcare industry must prioritize cybersecurity and provide the funding required to develop critical systems for safeguarding patients and their data. The study's findings underscore the need for standardized cybersecurity practices in healthcare to address inconsistencies in measures across organizations. Adequate and ongoing investment in cybersecurity infrastructure is imperative to counter increasingly sophisticated cyberattacks. Additionally, protecting patient data and maintaining trust within the healthcare sector are ethical imperatives that should guide industry actions. By embracing these implications, the healthcare industry can enhance patient security, financial stability, and ethical integrity.

GIS-Enabled Multi-Criteria Assessment for Hospital Site Suitability: A Case Study of Tehran

In developing countries, the interaction between rapid urban expansion and population growth brings forth a host of challenges, particularly concerning essential services like healthcare. While interest in site suitability analysis for identifying optimal hospital locations to ensure equitable and secure healthcare access is on the rise, the absence of a holistic study that encompasses social and environmental aspects in the assessment of hospital site suitability is evident. The objective of this research is to introduce a hybrid methodology that combines Geographic Information Systems (GIS) with Multi-Criteria Decision-Making (MCDM) weighting methods. This methodology aims to create hospital site suitability maps for districts 21 and 22 in Tehran, taking into account socio-environmental factors. In addition to the conventional Analytical Hierarchical Process (AHP) weighting method, this study employs two relatively less-explored methods, the Best-Worst Method (BWM) and Step-wise Weight Assessment Ratio Analysis (SWARA), to enhance the analysis of hospital site suitability. In the SWARA method, there are minimal variations in weights among criteria, indicating that all socio-environmental factors (e.g., distance from existing hospitals, distance from main roads, distance from green spaces) hold significant importance in the decision-making process. Additionally, the findings indicate that the western part of the study area is the most suitable location for the construction of a new hospital. To achieve the average hospital bed availability in Tehran, an additional 2206 beds are required in the studied area, in addition to the existing facilities. Considering the ongoing urban development, population growth, and the potential for natural disasters and epidemics, it becomes essential to enhance the healthcare system by increasing the number of hospitals and available hospital beds. The sensitivity analysis showed that GIS-based SWARA-WLC was the most suitable and stable model for determining hospital site suitability in the study area. This methodology can be adapted for use in other regions and further improved by incorporating additional criteria. In conclusion, the study recommended three specific alternative sites for establishing a new hospital in the study area.

Predicting Vulnerabilities in the Delivery of Secure Healthcare Supply Chain Services

The COVID-19 pandemic exposed the weaknesses in healthcare supply chains, leading to shortages, increased costs, and untrustworthy vendors. This has put patients at risk for treatment delays, worse care, and higher death rates. The healthcare supply chain's dependence on single sourcing has made these issues worse. Geopolitical conflicts, climatic catastrophes, data privacy issues, cybersecurity threats, and counterfeit goods are emerging risks. Resilient solutions are needed due to the complex ecosystem of manufacturers, wholesalers, regulatory organizations, and healthcare providers. Supply Chain Resilience (SCRES) is crucial for uninterrupted healthcare services, with redundancy, visibility, and adaptability being key components. Future research directions include blockchain integration, data analytics, and regulatory frameworks to improve resilience. Successful mitigation solutions require understanding interdependence, resilience measurements, and behavioral elements. Addressing these vulnerabilities is crucial for patient safety and healthcare effectiveness.