Healthcare Data Security Research Articles

Improving Healthcare Data Security and Privacy With Blockchain technology using SHA-256 Algorithm

Block chain technology has the potential to completely transform healthcare management systems by enhancing data security, interoperability, and integrity because to its decentralized and unchangeable structure. Sensitive patient information is more difficult for bad actors to breach thanks to the SHA-256 algorithm, which improves data security. Interoperability between diverse healthcare systems is facilitated by smart contracts and standardized data formats. However, issues with user adoption, scalability, and regulatory compliance also face block chain implementation in the healthcare industry. This study offers important insights into the changing environment of healthcare data management by highlighting the possible advantages and challenges of integrating block chain technology in healthcare management systems.

A Blockchain Based Security system framework in Healthcare Domain using IoT

In the contemporary healthcare domain, the integration of Internet of Things (IoT) technologies has revolutionized patient care, enabling real-time monitoring, enhanced diagnostics, and personalized treatment plans. However, this digital transformation is accompanied by unprecedented challenges in ensuring the security and privacy of sensitive healthcare data. Traditional cybersecurity measures often fall short in addressing the complexity and dynamism of IoT ecosystems. This research introduces a novel blockchain-based security system framework specifically designed for the healthcare sector utilizing IoT. By leveraging the inherent security, transparency, and immutability features of blockchain technology, the proposed framework provides a robust solution to safeguard healthcare data integrity, ensure privacy, and enable secure patient data sharing among authorized entities. Through a comprehensive system architecture, this paper delineates the integration of IoT devices with a blockchain network, highlighting the deployment of smart contracts for automated access control, and data encryption mechanisms to preserve confidentiality. An empirical evaluation demonstrates the framework's effectiveness in enhancing data security while maintaining system efficiency. The discussion extends to the implications of this framework for the future of healthcare IT security, addressing potential challenges and suggesting avenues for further research. This study not only contributes to advancing the state of cybersecurity in healthcare IoT but also lays the groundwork for future innovations in the field.

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

Enhancing Patient Consent Management through Blockchain Technology: A Promising Approach for Healthcare Data Security

In contemporary healthcare systems, ensuring patient consent management while safeguarding data privacy and security is a paramount concern. Traditional approaches to managing patient consent often face challenges related to security vulnerabilities, data breaches, and lack of transparency. This research paper proposes a novel approach to address these challenges by leveraging blockchain technology for patient consent management. Blockchain, with its decentralized and immutable ledger, offers a promising solution for securely recording and managing patient consent in healthcare settings. This paper presents a detailed exploration of the concept, design, and implementation of a blockchain-based patient consent management system. Through a comprehensive literature review, methodology description, and evaluation of the proposed system, this research demonstrates the potential benefits of blockchain technology in enhancing patient privacy and data security. The findings of this study contribute to the advancement of healthcare data management practices and provide valuable insights for future research and development in this evolving field. Keywords— Patient consent management, Blockchain technology, Healthcare data security, Privacy protection, Decentralization, Immutable ledger, Transparency, System architecture, Data integrity, Healthcare information management

Quantum-based privacy-preserving techniques for secure and trustworthy internet of medical things an extensive analysis

The advent of Quantum Key Distribution (QKD) and quantum-based privacy-preserving techniques has ushered in a new era for securing communication channels within the Internet of Medical Things (IoMT) systems. We examine the fundamentals, uses, and ramifications of quantum cryptography in relation to healthcare data security in this thorough investigation. The journey commences with an in-depth overview of quantum cryptography, unraveling the concepts of superposition, entanglement, and quantum gates that form the bedrock of quantum computing. As we proceed, we investigate the uses of quantum cryptography, highlighting its contribution to resolving the particular issues brought about by the data-intensive and networked character of IoMT systems. The relevance of QKD in ensuring secure communication within IoMT is meticulously dissected, with case studies and experiments demonstrating the practicality and effectiveness of quantum-based privacy-preserving techniques. From telemedicine networks to wearable health devices, each case study offers valuable insights into the application of quantum-safe cryptography, showcasing its ability to fortify data integrity and confidentiality. A meticulous survey of existing research, coupled with an analysis of advancements in quantum cryptography, provides a panoramic view of the current landscape. From hardware limitations to distance constraints, the study navigates through challenges and breakthroughs, offering a roadmap for the future integration of quantum technologies into IoMT systems.Comparing quantum-based approaches with classical cryptography unveils a nuanced landscape of computational complexity, key distribution methodologies, and real-time encryption considerations. This comparative analysis serves as a guide for healthcare practitioners and technologists in making informed decisions regarding the adoption of quantum-based privacy-preserving techniques in IoMT environments. The case studies and experiments collectively paint a picture of the practicality and promise of quantum-based privacy-preserving techniques in IoMT scenarios. Anticipating future advancements, the exploration extends into quantum hardware improvements, standardization efforts, and the integration of quantum technologies with emerging trends like edge computing and blockchain. As the healthcare industry stands on the cusp of a quantum revolution, the comprehensive insights provided herein offer a foundation for understanding, implementing, and shaping the future of secure healthcare communication through the lens of quantum cryptography.

A secure and privacy preserved data aggregation scheme in IoMT

The Internet of Things (IoT) is a network of intelligent devices especially in healthcare-based systems. Internet of Medical Things (IoMT) uses wearable sensors to collect data and transmit to central repositories. The security and privacy of healthcare data is a challenging task. The aim of the study is to provide a secure data sharing mechanism. The existing studies provide secure data sharing schemes but still have limitations in terms of hiding the patient identify in the messages exchanged to upload the data on central repositories. This paper presents a Secure Aggregated Data Collection and Transmission (SADCT) that provides anonymity for the identities of patient's mobile device and the intermediate fog nodes. Our system involves an authenticated server for node registration and authentication by saving security credentials. The proposed scheme presents the novel data aggregation algorithm at the mobile device, and the data extraction algorithm at the fog node. The work is validated through extensive simulations in NS along with a security analysis. Results prove the supremacy of SADCT in terms of energy consumption, storage, communication, and computational costs.

Secure Healthcare Model Using Multi-Step Deep Q Learning Network in Internet of Things

Internet of Things (IoT) is an emerging networking technology that connects both living and non-living objects globally. In an era where IoT is increasingly integrated into various industries, including healthcare, it plays a pivotal role in simplifying the process of monitoring and identifying diseases for patients and healthcare professionals. In IoT-based systems, safeguarding healthcare data is of the utmost importance, to prevent unauthorized access and intermediary assaults. The motivation for this research lies in addressing the growing security concerns within healthcare IoT. In this proposed paper, we combine the Multi-Step Deep Q Learning Network (MSDQN) with the Deep Learning Network (DLN) to enhance the privacy and security of healthcare data. The DLN is employed in the authentication process to identify authenticated IoT devices and prevent intermediate attacks between them. The MSDQN, on the other hand, is harnessed to detect and counteract malware attacks and Distributed Denial of Service (DDoS) attacks during data transmission between various locations. Our proposed method’s performance is assessed based on such parameters as energy consumption, throughput, lifetime, accuracy, and Mean Square Error (MSE). Further, we have compared the effectiveness of our approach with an existing method, specifically, Learning-based Deep Q Network (LDQN).

Enhancing healthcare services through cloud service: a systematic review

Although cloud-based healthcare services are booming, in-depth research has not yet been conducted in this field. This study aims to address the shortcomings of previous research by analyzing all journal articles from the last five years using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) systematic literature review methodology. The findings of this study highlight the benefits of cloud-based healthcare services for healthcare providers and patients, including enhanced healthcare services, data security, privacy issues, and innovative information technology (IT) service delivery models. However, this study also identifies challenges associated with using cloud services in healthcare, such as security and privacy concerns, and proposes solutions to address these issues. This study concludes by discussing future research directions and the need for a complete solution that addresses the conflicting requirements of the security, privacy, efficiency, and scalability of cloud technologies in healthcare.

Big Data Security on Hadoop Open Source Frame for Healthcare Data Management using One-Time-Pad Encryption Algorithm

The study elicited knowledge about the factors associated with one-time pad encryption/decryption with big data in healthcare; formulate an assembled algorithms model for one-time pad encryption; design and implement the system and evaluating the system performance with the view implementing big data security on Hadoop open-source framework for healthcare data. Literature was sourced to investigate the factors associated with healthcare security attacks and various consequences of breach of data. An assembled algorithm model was formulated using mathematical theory of one-time pad encryption and a model was designed using Universal Modelling Language (UML) and implemented using python programming language, Distributed File System of Hadoop, Yet Another Resource Negotiator called YARN; encryption time and decryption time was adopted for the performance metrics deployed for the evaluation of the developed system. The result showed that as the size of the files increased, the encryption/decryption time keeps increasing as well. While carryout the algorithm evaluation, two different values (file sizes) were used for testing on the Hadoop framework.Securing the healthcare (Ebola) big-data, it was observed that OTP encryption/decryption performed better compared to AES encryption/decryption in term of computational processing time of the healthcare big-data considered. Considering before/after downloading, it was observed that there was need for authentication for another level of security towards securing healthcare records on HDFS. The study concluded that, big data analytics on Hadoop is ideal for today’s big healthcare data and also that One Time Pad encryption algorithm is sufficient to provide needed big healthcare data security.

Healthcare Data Security in Cloud Storage Using Light Weight Symmetric Key Algorithm

Now-a-days, healthcare monitoring system is very much important system in the medical field to know the patient‘s health status immediately. In the proposed system, the sensors are fixed over the patient’s body or placed at some distances around the body to collect patient’s significant parameters like blood pressure, temperature, heart beat rate, etc. These parameters are collected by the healthcare professionals through some connectivity mechanisms like Bluetooth, ZigBee, etc. These significant data will be outsourced to the cloud storage in a secure way to avoid attack from the attackers. So, we need some protection mechanism to safeguard this information. This article proposes a light weight cryptographic algorithm (symmetric key) via random number key generation using Diffie-Hellman key exchange based on Elliptic Curve (ECDH) cryptography. As a result of substituting bytes (S-box) and folding (horizontal and vertical) operations, the proposed symmetric key algorithm achieves the foremost properties of cryptography such as confusion and diffusion quite well. Experimental results showed that the overall execution time of the proposed algorithm is superior to the standard Advanced Encryption Standard (AES) algorithm. The throughput rate of the proposed algorithm is 20.525095 KB/seconds whereas for the standard AES algorithm throughput rate is 18.727215 KB/seconds. So, the proposed algorithm is faster than the existing AES algorithm. Moreover, the construction of S-box, IS-box and the key generation procedures are entirely different in the proposed algorithm so, it increases the complexity for the attacker and it will create confusion to the attacker

Enhancing Healthcare Data Security and Disease Detection Using Crossover-Based Multilayer Perceptron in Smart Healthcare Systems

Enhancing Healthcare Data Security and Disease Detection Using Crossover-Based Multilayer Perceptron in Smart Healthcare Systems

Fortifying Healthcare Data Security in the Cloud: A Comprehensive Examination of the EPM-KEA Encryption Protocol

Fortifying Healthcare Data Security in the Cloud: A Comprehensive Examination of the EPM-KEA Encryption Protocol

Central Medical Centre Healthcare Data Security with Lightweight Blockchain Model in IoT Sensor Environment

Data security involves implementing measures to protect digital information from unauthorized access, disclosure, alteration, or destruction, ensuring the confidentiality, integrity, and availability of sensitive data. Hence, this paper addresses the critical imperative of ensuring robust data security within the healthcare domain. With the proliferation of digital technologies and interconnected systems, safeguarding patient information has become paramount. The study comprehensively reviews existing strategies for data security in healthcare, including encryption, access controls, and secure authentication mechanisms. It delves into the unique challenges faced by healthcare organizations, such as the interoperability of systems, compliance with regulatory frameworks and the increasing sophistication of cyber threats. The paper emphasizes the significance of fostering a culture of cybersecurity awareness among healthcare professionals and implementing proactive measures to mitigate vulnerabilities. Future directions in healthcare data security, including the integration of emerging technologies like blockchain and artificial intelligence, are also explored. By providing a holistic overview of data security challenges and solutions in healthcare, this paper contributes to the ongoing efforts to fortify the confidentiality, integrity, and availability of sensitive patient data in the digital age. The effectiveness of two medical treatments (Treatment A and Treatment B) is assessed based on the responses of 20 participants, revealing subtle yet notable variations in efficacy. Additionally, the security scores of Healthcare Systems A and B are evaluated, demonstrating a marginal superiority of System B. A detailed classification of cyber attacks, ranging from phishing and malware infections to insider threats and zero-day exploits, sheds light on the diverse nature of cybersecurity challenges. The findings underscore the importance of a multifaceted approach to medical data security, emphasizing the need for robust treatments, secure healthcare systems, and targeted defense strategies against evolving cyber threats. This paper contributes valuable insights to the ongoing discourse on bolstering cybersecurity measures in healthcare to safeguard patient information and fortify the integrity of healthcare systems in an increasingly digital landscape.

Effective Data Aggregation Moel for the Healthcare Data Transmission and Security in Wireless Sensor Network Environment

Data aggregation is the process of collecting and combining information from multiple sources to provide a unified view or summary. In various fields such as statistics, economics, and data analysis, aggregating data helps reveal patterns, trends, or general insights that may not be apparent when examining individual data points. Aggregated data can provide a more comprehensive perspective, facilitating decision-making and strategic planning. This paper explores the application of Sugeno Fuzzy Model Monkey Swarm Optimization (SFMMsO) in healthcare, specifically focusing on data aggregation and security within Wireless Sensor Networks (WSN). The study demonstrates SFMMsO’s efficacy in aggregating patient health data, generating comprehensive Aggregated Health Indices. It also highlights SFMMsO’s optimization capabilities, refining decision variables to enhance healthcare algorithm performance. The paper addresses the critical dimension of security, showcasing SFMMsO’s adaptability in optimizing security parameters and assessing its vulnerability to various attack types. The findings underscore SFMMsO’s potential as a robust tool for healthcare applications, emphasizing the need for proactive security measures to fortify its resilience against potential threats. This study contributes valuable insights into the intersection of optimization algorithms, data aggregation, and security in healthcare, paving the way for advancements in utilizing SFMMsO for secure and comprehensive healthcare systems.

Enhancing healthcare data security: a two-step authentication scheme with cloud technology and blockchain

In the modern world, medical data leakage has many external and internal threats. Information systems of medical organizations are constantly subject to various types of cyber-attacks and unauthorized penetration attempts, which leads to the publication of patient medical data online. Existing authentication schemes using blockchain technologies in medical organization systems ensure the integrity of medical data and secure access to patient data. However, one of the serious reasons for unauthorized access to the healthcare system is the human factor, which manifests itself in a negligent attitude towards account security, non-compliance with the rules and policies of information security, and transferring to third parties personal login details to the information system of a medical organization. This paper proposes a solution to this problem through an improved two-step authentication scheme using cloud technology and blockchain. The combined use of cloud technologies and blockchain is a distinctive feature of the proposed authentication scheme since it provides two levels of protection: 1) two-step authentication, the second stage of which includes biometrics through a mobile application. It prevents unauthorized access to the system by third parties; 2) cloud encryption keys for decrypting medical data, which are also accessed through the user's biometrics. The practical part of the paper includes the implementation of biometric login in Python using the OpenCV library. As a result of the practical part, unique fingerprint samples were obtained. The biometric user verification algorithm is designed for a mobile application, which we plan to implement in the future

Design of blockchain-enabled secure smart health monitoring system and its testbed implementation

Smart healthcare technology is transforming from the traditional healthcare system in every manner conceivably. Smart healthcare provides several advantages over the existing approaches. However, it suffers from healthcare data security and privacy issues. As the Internet attackers may get access to sensitive healthcare data through the use of various types of cyber attacks. In this paper, an architecture of a blockchain-enabled secure smart health monitoring system has been presented (in short, it is called as BSSHM). BSSHM consists of various health data monitoring sensors, i.e., temperature, heartbeat, etc., which monitor the real time health data of the different patients. The healthcare data of the patients can be transmitted to the connected health servers in a secure way, where this data can be stored securely for its various uses. The formal security verification of the proposed BSSHM is also done through the widely-accepted Scyther tool. It has been proved that BSSHM is able to defend various potential attacks.

Enhancing Healthcare Data Security in IoT Environments Using Blockchain and DCGRU with Twofish Encryption

Enhancing Healthcare Data Security in IoT Environments Using Blockchain and DCGRU with Twofish Encryption

Leveraging Smart Contracts for Decentralized Healthcare Data Sharing

Abstract: Healthcare data sharing is essential for enhancing healthcare service quality and efficiency. However, traditional healthcare data sharing systems are often centralized, opaque, and inefficient. This can pose security and privacy concerns, as well as delays and errors in data access. The integration of blockchain-based smart contracts into the healthcare sector has introduced a transformative paradigm for decentralized healthcare data sharing. This research paper explores the multifaceted landscape of leveraging smart contracts to enhance the security, efficiency, and accessibility of healthcare data sharing. By offering secure, transparent, and automated mechanisms, smart contracts address critical challenges in patient data management, consent verification, and interoperability across healthcare systems.

Healthcare Data Security Using Lightweight Protocol for Cyber Physical System

Healthcare Data Security Using Lightweight Protocol for Cyber Physical System

Design and Testbed Experiments of User Authentication and Key Establishment Mechanism for Smart Healthcare Cyber Physical Systems

Smart healthcare technologies transform the traditional healthcare system in all possible ways. Smart healthcare has enormous number of benefits over the traditional system. However, at the same time, it also suffers from some healthcare data security and privacy related issues as the potential Internet attackers may get access to the sensitive healthcare through the deployment of various kinds of attacks. To mitigate these issues, in this paper, a new lightweight remote user authentication and key establishment scheme (in short, UAKM-SH) has been proposed by making the use of lightweight cryptographic operations. The security analysis of UAKM-SH is conducted to prove its robustness against various possible attacks. A detailed comparative study among the proposed UAKM-SH and other existing schemes shows that UAKM-SH performs better in terms of computation cost, communication cost, and the offered security and functionality features. Finally, the testbed implementation of UAKM-SH is given to observe its behavior in the real time scenario.