Privacy Of Medical Data Research Articles

GAN-Based Novel Approach for Generating Synthetic Medical Tabular Data

The generation of synthetic medical data has become a focal point for researchers, driven by the increasing demand for privacy-preserving solutions. While existing generative methods heavily rely on real datasets for training, access to such data is often restricted. In contrast, statistical information about these datasets is more readily available, yet current methods struggle to generate tabular data solely from statistical inputs. This study addresses the gaps by introducing a novel approach that converts statistical data into tabular datasets using a modified Generative Adversarial Network (GAN) architecture. A custom loss function was incorporated into the training process to enhance the quality of the generated data. The proposed method is evaluated using fidelity and utility metrics, achieving “Good” similarity and “Excellent” utility scores. While the generated data may not fully replace real databases, it demonstrates satisfactory performance for training machine-learning algorithms. This work provides a promising solution for synthetic data generation when real datasets are inaccessible, with potential applications in medical data privacy and beyond.

New Image Crypto-Compression Scheme Based on Ecc and Chaos Theory for High-Speed and Reliable Transmission of Medical Images in the IOMT

Abstract The rapid advancement of IoT has significantly transformed the healthcare sector, leading to the emergence of the Internet of Medical Things (IoMT). Ensuring the security and privacy of medical data is crucial when integrating with smart and intelligent sensor devices within the hospital environment. In this context, we propose a lightweight crypto-compression scheme based on Elliptic Curve Cryptography (ECC) and Chaos theory to secure the medical images in IoMT Applications. The primary innovation in this method involves generating dynamic S-box and keys using the ECC mechanism and PieceWise Linear Chaotic Map (PWLCM). The Wavelet Transform Technology is employed in compression, and the compressed images are secured within an IoT framework. The proposed methodology has been performed in the experiments on various medical images. The findings and Security analysis reveal that the proposed method is more powerful and useful for secure medical image transmission in the IoT ecosystem.

IZKP-AKA: A secure and improved ZKP-AKA protocol for sustainable healthcare

The use of IoT in healthcare has undoubtedly brought many significant adaptations and benefits that changed medical facilities. However, the possibility of unauthorized access to private medical data is a serious issue that requires appropriate attention to protect the user’s privacy. Recently, a proposed scheme by Gurjot et al. suggested an authentication mechanism to provide anonymity and other security characteristics. We did the security analysis and informally proved that their scheme is prone to various attacks, such as failure to offer perfect forward secrecy, ephemeral secret leakage, traceability, replay, stolen device attacks, and also face desynchronization issues. These issues make the proposed scheme unsuitable for the healthcare system. Therefore, there is an impelling need to design an authentication mechanism that can restrict the attacker from getting any sensitive information. Considering the above requirements, we present a novel Zero Knowledge Proof based Authenticated Key Agreement (ZKP-AKA) protocol. The security of our proposed authentication mechanism is examined using the informal (non-mathematical) and formal (Scyther tool) security verification to confirm that the proposed protocol offers the prominent security features mentioned above. We also measure the performance to show that our proposed mechanism is suitable for IoT devices in the healthcare intelligent system by doing a comparative analysis with its competitors in terms of communication, computational, message exchange and energy consumption costs.

Keeping medical information safe and confidential: a qualitative study on perceptions of Israeli physicians

BackgroundPatients expect their information to remain confidential, and physicians have a legal and ethical obligation to keep it this way. Confidentiality is not just a legal requirement but a crucial element in establishing trust between patients and healthcare providers. Patients must feel confident that their personal and medical information is kept confidential and shared only with those who need to know. Previous studies have primarily concentrated on patients’ perceptions of medical confidentiality, data privacy, and data protection issues. However, research on the practical practices and perceptions of medical confidentiality among hospital physicians is scant, underscoring the need for a deeper understanding of this critical issue.MethodsThrough qualitative methods and as part of a large-scale study on aspects of patient dignity and responsiveness in Israel, physicians shared their views and practices on managing medical information.ResultsThe study revealed the practical challenges physicians face in upholding various aspects of data protection within hospital settings. These challenges, strategies, and deviations from data protection principles that physicians discussed are of significant practical relevance. The importance of patient consent and the practical measures for safeguarding patient information were also highlighted. While physicians acknowledged the importance of protecting patient information, they also grappled with the realities of doing so in a complex healthcare environment. In future healthcare policies, it is critical to ensure robust measures are in place to safeguard and uphold medical confidentiality. These can include specific measures to increase compliance, such as regularly monitoring compliance with confidentiality policies, producing safe and anonymous channels to voice concerns, and enforcing consequences for any breaches to ensure accountability.ConclusionsWhile protecting medical information has emerged as an important goal, it is equally crucial to strike a balance between the need to share information to advance and provide quality medical care. Physicians and policymakers must navigate this delicate balance. Additionally, organizations should strengthen compliance to enhance their monitoring and enforcement of confidentiality policies. Ineffective implementation of medical confidentiality leads to theoretical guidelines that do not translate effectively into practice.

Bilateral Proxy Federated Domain Generalization for Privacy-preserving Medical Image Diagnosis.

Contemporary domain generalization methods have demonstrated effectiveness in aiding the generalized diagnosis of medical images with multi-source data by joint optimization. However, the centralized training paradigm employed by these approaches becomes infeasible when data are non-shared across domains due to the high privacy of medical data. Despite attempts by existing federated domain generalization methods to address this issue, the simultaneous attainment of strict privacy protection and a satisfactory level of generalization ability on out-of-distribution data remains a persistent challenge. In this paper, to tackle this challenging problem, we propose a novel approach called the Bilateral Proxy Framework (BPF). The BPF leverages the client-side proxies to facilitate the strict privacy-preserving communications with the server and ensure smoother and more stable convergences of local models through mutual distillation. Meanwhile, the server-side proxy adopts a distance-based strategy and a parameter moving average scheme, which enhances the stability and robustness of the global model, particularly by averting abrupt parameter changes that could result in fluctuations or overfitting. Through these advancements, our framework strives to enhance the generalization capability of the global model, enabling more accurate and reliable medical image diagnosis in federated settings. The effectiveness of our method is demonstrated with superior performance over state-of-the-arts on both simulated and real-world distribution medical image diagnosis tasks.

An advanced cryptographic scheme for DICOM medical image encryption using a novel spatiotemporal chaotic lattice

Ensuring the privacy and security of medical imaging data is crucial. Most existing methods focus on encrypting images in common formats, neglecting the unique challenges posed by medical formats. In this paper an advanced encryption algorithm based on a multi-dimensional and multi-parametric spatiotemporal coupled chaotic lattice (Double-MsCcL) system is proposed. The Double-MsCcL system enhances the complexity of chaotic sequences, and provides a robust foundation for medical image encryption. The encryption method proposed combines double-layer random DNA diffusion and fractal random permutation. Medical information is embedded in the image pixels, serving as the cryptosystem key. The designed double-layer DNA random diffusion algorithm alters pixel values, while the fractal random permutation algorithm disrupts pixel positions. This proposed approach effectively hides sensitive information, strengthens resistance to plaintext attacks, and address the often-overlooked Uint16 format of DICOM images. Comprehensive performance evaluations have shown that the proposed encryption scheme offers significant advantages in protecting sensitive medical imaging data. Our algorithm exhibits better performance in resisting plaintext analysis, statistical, and differential attacks, demonstrating significant security compared to existing state-of-the-art methods, which can be widely applied in the field of modern medical data protection.

Blockchain‐Enabled Decentralized Healthcare Data Exchange: Leveraging Novel Encryption Scheme, Smart Contracts, and Ring Signatures for Enhanced Data Security and Patient Privacy

ABSTRACTThe healthcare industry has undergone a digital transformation in recent years, with the adoption of electronic health records (EHRs) becoming increasingly prevalent. While this digitization offers various advantages, concerns regarding the security and privacy of sensitive medical data have also intensified. Data breaches and cyber‐attacks targeting healthcare organizations have underscored the need for robust solutions to protect patient data. Blockchain technology has emerged as a promising solution due to its decentralized and immutable nature, which ensures secure and transparent data recording. This paper proposes a novel approach that combines blockchain with advanced encryption scheme and privacy protection technique to establish a secure and privacy protected medical data sharing environment. The proposed system consists of three phases such as initialization phase, data processing phase, and authentication phase. The hybrid Feistal‐Shannon homomorphic encryption algorithm (HFSHE) is proposed to encrypt the medical data to ensure data confidentiality, integrity, and availability. Ring signature is integrated to the system to provide additional anonymity and protect the identities of the participants involved in data transactions. In addition, the smart contract developed performs authentication checks on users, generates a time seal, and verifies the ring signature. Through this enhancement, the system becomes more resilient to both external and internal threats, enhancing overall security as well as privacy. A comprehensive security analysis is conducted to compare the proposed method's performance against existing techniques. The results demonstrate the effectiveness of the proposed approach in safeguarding sensitive medical information within the blockchain ecosystem.

Multicenter Analysis of Emergency Patient Severity through Local Model Evaluation Client Selection: Optimizing Client Selection Based on Local Model Evaluation

In multi-institutional emergency room settings, the early identification of high-risk patients is crucial for effective severity management. This necessitates the development of advanced models capable of accurately predicting patient severity based on initial conditions. However, collecting and analyzing large-scale data for high-performance predictive models is challenging due to privacy and data security concerns in integrating data from multiple emergency rooms. To address this, our work applies federated learning (FL) techniques, maintaining privacy without centralizing data. Medical data, which are often non-independent and identically distributed (non-IID), pose challenges for existing FL, where random client selection can impact overall FL performance. Therefore, we introduce a new client selection mechanism based on local model evaluation (LMECS), enhancing performance and practicality. This approach shows that the proposed FL model can achieve comparable performance to centralized models and maintain data privacy. The execution time was reduced by up to 27% compared to the existing FL algorithm. In addition, compared to the average performance of local models without FL, our LMECS improved the AUC by 2% and achieved up to 23% performance improvement compared to the existing FL algorithm. This work presents the potential for effective patient severity management in multi-institutional emergency rooms using FL without data movement, offering an innovative approach that satisfies both medical data privacy and efficient utilization.

Federated semi-supervised representation augmentation with cross-institutional knowledge transfer for healthcare collaboration

In the healthcare field, cross-institutional collaboration can fasten medical research progress. Vertical federated learning (VFL) addresses data heterogeneity across multiple medical institutions while ensuring medical data privacy, thereby enhancing the accuracy of disease diagnoses and treatments. However, traditional VFL only benefits from aligned samples, thereby limiting its applicability due to constrained sample sizes, and a large amount of non-aligned data remains untapped, resulting in wasted data. To exert full leverage on the value of all data obtained from medical institutions, this paper proposes a federated healthcare collaborative framework based on semi-supervised representation augmentation mechanism with cross-institutional knowledge transfer (CrossKT-FRA). Specifically, the developed method comprises three steps. First, the federated representations of shared data (aligned data) among medical institutions are extracted through efficient vertical federated representation learning (FRL) methods. Second, the federated knowledge contained in federated representations and potential labels derived through recurrent learning assist local shared data representations in performing supervised augmented learning. Finally, the federated knowledge is transferred indirectly from the representation augmentation module for shared data to the unsupervised representation augmentation module for local private data (non-aligned data). The experimental results show the effectiveness of the proposed knowledge transfer mechanism, whether applied independently or used to enhance VFL on medical datasets. Our findings contribute to a deeper theoretical understanding of VFL, further facilitating the utilization of high-value medical data. By promoting cross-institutional and cross-disciplinary collaboration in healthcare data sharing, our study enhances the quality efficiency of medical services, thereby accelerating the development of interdisciplinary medical research. Code is available at https://github.com/LieLieLieLieLie/CrossKT-FRA.

Enhancing healthcare security: Time‐based authentication for privacy‐preserving IoMT sensor monitoring framework leveraging blockchain technology

SummaryThe rapid progression of the Internet of Things and its increasing use in healthcare has generated considerable concerns over the safeguarding and privacy of vital medical data. In response to these issues, blockchain has surfaced as a possible remedy, offering transparent, immutable, and decentralized storage. Nevertheless, conventional blockchain‐based systems still encounter constraints in maintaining anonymity, confidentiality, and privacy. Hence, this article suggests a framework based on a secure consortium blockchain that prioritizes data privacy and employs time‐based authentication to streamline patient data monitoring. First, we employ time‐based authentication to verify the identities of authorized users. This process utilizes the NIK‐512 hashing algorithm in conjunction with passwords and registered timestamps, which strengthens the confidentiality of data. Patient information undergoes encryption before transmission within the network. Further, our framework introduces a sensor registration service that the trusted node employs to assign a distinct identity to each sensor connected to a patient. The implementation of data processing and filtering techniques at the edge layer serves the purpose of mitigating disturbances that may occur during the collection of sensor‐based data. Finally, a comprehensive evaluation of performance and security has been carried out with various metrics. The findings indicate that the proposed solution effectively enhances the management of Internet of Medical Things data by providing improved privacy and security.

Understanding the Perspectives of Hail City Population on the Confidentiality and Privacy of Digital Health and Medical Information.

Background In the era of rapid digital advancement, the confidentiality and privacy of digital health and medical data have become paramount concerns. This study investigates the perspectives of individuals residing in Hail City regarding these critical issues, with a particular emphasis on the influence of demographic factors such as age, gender, and computer proficiency on individuals' discomfort with health professionals using computers and their trust in researchers.Gaining a deeper understanding of these factors is vital for the development of targeted interventions aimed at enhancing patient comfort and trust in digital health/medical technologies. Methodology This study employed a descriptive cross-sectional design, involving a survey of 775 individuals aged 18 and above in Hail City. The questionnaire was designed to gather information on participants' demographic characteristics, computer proficiency, experiences with digital health and medical information, and perceptions of health information safety and privacy. To examine the associations and predictive relationships between variables, chi-square tests, correlation analyses, and logistic regression were performed. Results Significant associations were found between gender and discomfort with health professionals using computers (chi-square = 60.29, p < 0.0001), and between age and trust in researchers regarding the privacy of medical information (chi-square = 50.14, p < 0.0001). Positive correlations were observed between computer proficiency and perception of health information safety (r = 0.12, p = 0.0002), while a negative correlation was found between computer ownership and avoidance of medical tests due to privacy concerns (r = -0.08, p = 0.03). Logistic regression analysis identified age, gender, and computer proficiency as significant predictors of discomfort with health professionals using computers. The findings highlight the crucial role that demographic factors play in shaping attitudes toward the privacy and security of digital health and medical information. Conclusions The findings of this study highlight the crucial role that demographic factors play in shaping attitudes toward the privacy and security of digital health and medical information. Gender and age were found to significantly influence individuals' levels of discomfort and trust, while computer proficiency was shown to enhance perceptions of safety. Based on these findings, the researchers recommend implementing targeted interventions, such as gender-sensitive training programs and initiatives, to enhance digital literacy and improve patient comfort and trust in digital health technologies.

Federated Learning with Homomorphic Encryption for Ensuring Privacy in Medical Data

Federated Learning (FL) is a machine learning methodology that allows remote devices to collectively train a learning system without sharing their data. FL-based methods provide enhanced secure privacy by transmitting only localized model variables, learned with local information, from dispersed devices to a centralized controller. There is a potential for a centralized server or malicious individuals to deduce or get sensitive private data by analyzing the structure and variables of regional learning networks. This study incorporates the FL process into the deep learning process of medical prototypes in an Internet of Things (IoT)-based medical facility. A Secured Medical Homomorphic Encryption Algorithm (SMHEA) is proposed in this research to ensure medical data privacy. Cryptographic primitives, such as masking and homomorphic cryptography, are used to enhance the security of local modeling. This prevents adversaries from deducing confidential health information via assaults like model restoration or modeling inversion. The primary determinant for assessing the regional modeling's contributions to the universal model during each training stage is the quality of the databases possessed by various individuals rather than the typically used metric of database dimension in deep learning. A dropout-tolerant approach is suggested, where the FL procedure would continue as long as the total amount of online customers remains over a certain level. By doing a security evaluation, it is evident that the suggested approach effectively ensures data privacy. Theoretical analysis is conducted on accuracy, computation time, and communication error. An example of clinical applications is the categorization of skin lesions using training photos from the HAM10000 medical database. The experimental findings demonstrate that the suggested system had favorable performance and privacy preservation outcomes compared to current methods.

A Review of Blockchain-Based Research on E-Health Data Sharing

With the development of medical data informatization, the data security, data privacy, and data controllability of electronic medical data with patient's personal privacy have gained social attention. Blockchain technology has decentralized characteristics, and the development of blockchain technology provides an effective solution for the secure sharing of e-medical data. Many scholars have proposed e-medical data security sharing solutions based on blockchain technology, and these solutions have attempted to solve the security problems encountered in sharing traditional e-medical data. By summarizing and analyzing the schemes in this field, this study analyzes the advantages, disadvantages, and challenges that the current mainstream schemes have, mainly in terms of blockchain data security storage, access control security sharing, and so on, in order to promote the further development of the e-medical data security sharing scheme based on blockchain technology. This study analyzes the blockchain-based e-medical data security sharing scheme proposed by scholars by analyzing the blockchain technology, cryptography technology, smart contract technology, P2P network technology and other related topics. Through systematic analysis and comparison, it explores the challenges of the current research and provides references for the future proposals of blockchain technology-based schemes.

Predicting treatment response in multicenter non-small cell lung cancer patients based on federated learning

BackgroundMulticenter non-small cell lung cancer (NSCLC) patient data is information-rich. However, its direct integration becomes exceptionally challenging due to constraints involving different healthcare organizations and regulations. Traditional centralized machine learning methods require centralizing these sensitive medical data for training, posing risks of patient privacy leakage and data security issues. In this context, federated learning (FL) has attracted much attention as a distributed machine learning framework. It effectively addresses this contradiction by preserving data locally, conducting local model training, and aggregating model parameters. This approach enables the utilization of multicenter data with maximum benefit while ensuring privacy safeguards. Based on pre-radiotherapy planning target volume images of NSCLC patients, a multicenter treatment response prediction model is designed by FL for predicting the probability of remission of NSCLC patients. This approach ensures medical data privacy, high prediction accuracy and computing efficiency, offering valuable insights for clinical decision-making.MethodsWe retrospectively collected CT images from 245 NSCLC patients undergoing chemotherapy and radiotherapy (CRT) in four Chinese hospitals. In a simulation environment, we compared the performance of the centralized deep learning (DL) model with that of the FL model using data from two sites. Additionally, due to the unavailability of data from one hospital, we established a real-world FL model using data from three sites. Assessments were conducted using measures such as accuracy, receiver operating characteristic curve, and confusion matrices.ResultsThe model’s prediction performance obtained using FL methods outperforms that of traditional centralized learning methods. In the comparative experiment, the DL model achieves an AUC of 0.718/0.695, while the FL model demonstrates an AUC of 0.725/0.689, with real-world FL model achieving an AUC of 0.698/0.672.ConclusionsWe demonstrate that the performance of a FL predictive model, developed by combining convolutional neural networks (CNNs) with data from multiple medical centers, is comparable to that of a traditional DL model obtained through centralized training. It can efficiently predict CRT treatment response in NSCLC patients while preserving privacy.

Gradient Matching Federated Domain Adaptation for Brain Image Classification.

Federated learning has shown its unique advantages in many different tasks, including brain image analysis. It provides a new way to train deep learning models while protecting the privacy of medical image data from multiple sites. However, previous studies suggest that domain shift across different sites may influence the performance of federated models. As a solution, we propose a gradient matching federated domain adaptation (GM-FedDA) method for brain image classification, aiming to reduce domain discrepancy with the assistance of a public image dataset and train robust local federated models for target sites. It mainly includes two stages: 1) pretraining stage; we propose a one-common-source adversarial domain adaptation (OCS-ADA) strategy, i.e., adopting ADA with gradient matching loss to pretrain encoders for reducing domain shift at each target site (private data) with the assistance of a common source domain (public data) and 2) fine-tuning stage; we develop a gradient matching federated (GM-Fed) fine-tuning method for updating local federated models pretrained with the OCS-ADA strategy, i.e., pushing the optimization direction of a local federated model toward its specific local minimum by minimizing gradient matching loss between sites. Using fully connected networks as local models, we validate our method with the diagnostic classification tasks of schizophrenia and major depressive disorder based on multisite resting-state functional MRI (fMRI), respectively. Results show that the proposed GM-FedDA method outperforms other commonly used methods, suggesting the potential of our method in brain imaging analysis and other fields, which need to utilize multisite data while preserving data privacy.

Cloud-based secure data storage and access control for internet of medical things using federated learning

PurposeThe purpose of this paper Computing is a recent emerging cloud model that affords clients limitless facilities, lowers the rate of customer storing and computation and progresses the ease of use, leading to a surge in the number of enterprises and individuals storing data in the cloud. Cloud services are used by various organizations (education, medical and commercial) to store their data. In the health-care industry, for example, patient medical data is outsourced to a cloud server. Instead of relying onmedical service providers, clients can access theirmedical data over the cloud.Design/methodology/approachThis section explains the proposed cloud-based health-care system for secure data storage and access control called hash-based ciphertext policy attribute-based encryption with signature (hCP-ABES). It provides access control with finer granularity, security, authentication and user confidentiality of medical data. It enhances ciphertext-policy attribute-based encryption (CP-ABE) with hashing, encryption and signature. The proposed architecture includes protection mechanisms to guarantee that health-care and medical information can be securely exchanged between health systems via the cloud. Figure 2 depicts the proposed work's architectural design.FindingsFor health-care-related applications, safe contact with common documents hosted on a cloud server is becoming increasingly important. However, there are numerous constraints to designing an effective and safe data access method, including cloud server performance, a high number of data users and various security requirements. This work adds hashing and signature to the classic CP-ABE technique. It protects the confidentiality of health-care data while also allowing for fine-grained access control. According to an analysis of security needs, this work fulfills the privacy and integrity of health information using federated learning.Originality/valueThe Internet of Things (IoT) technology and smart diagnostic implants have enhanced health-care systems by allowing for remote access and screening of patients’ health issues at any time and from any location. Medical IoT devices monitor patients’ health status and combine this information into medical records, which are then transferred to the cloud and viewed by health providers for decision-making. However, when it comes to information transfer, the security and secrecy of electronic health records become a major concern. This work offers effective data storage and access control for a smart healthcare system to protect confidentiality. CP-ABE ensures data confidentiality and also allows control on data access at a finer level. Furthermore, it allows owners to set up a dynamic patients health data sharing policy under the cloud layer. hCP-ABES proposed fine-grained data access, security, authentication and user privacy of medical data. This paper enhances CP-ABE with hashing, encryption and signature. The proposed method has been evaluated, and the results signify that the proposed hCP-ABES is feasible compared to other access control schemes using federated learning.

Secure medical data on cloud storage via DNA homomorphic encryption technique

BackgroundCloud computing offers considerable flexibility and financial savings to data owners for outsourcing their complicated data management systems from local sites to the commercial public cloud. MethodsHowever, an effective encryption scheme is needed to protect the private medical data of the user in the cloud without leaking it. Therefore, in this paper an efficient Deoxyribonucleic Acid Homomorphic Encryption algorithm (DNA-HE) has been proposed to encrypt and store the medical images in the cloud securely. Initially, the data is mapped to DNA sequences and then encrypted using homomorphic encryption. The key for the homomorphic algorithm is generated using Rider Optimization Technique which ensures security. It acts as a double encryption technique. The same procedure tends to decrypt the encrypted data. ResultsThe efficiency of the proposed technique is assessed employing several parameters such as execution time, encryption time, and decryption time. The experimental results shows that the proposed DNA-HE system reduces encryption time of 10 %, 20 %, and 35 % than OHE, HE and ECC algorithms.

MIWET: Medical image watermarking using encryption and fusion technique

Digital augmentation of contemporary healthcare has directed the expansion of ICT-based systems. At this stage, a variety of telemedical support has been implemented to ensure the provision of medical utilities. Data Privacy, reliability, and credibility are the three security prerequisites that demand constant monitoring and corrective measures. Watermarking and cryptographic techniques offer a potentially viable solution for effectively addressing the aforementioned challenges. This article presents a robust and secure watermarking procedure that effectively safeguards the privacy and accuracy of digital medical data. This work uses a hybridization of NSST and DTCWT to implement a multimodality image fusion method that generates the watermark image. Further, a combination of three transforms namely, NSST, BEMD, and MSVD is used to conceal the generated mark in the carrier image. Also, the weighting factor is computed using HOG-based optimization to achieve a balance between visual quality and robustness. Finally, the marked carrier image is encrypted with a suitable encryption technique for better security. The proposed framework’s versatility, embedding capacity, robustness, and imperceptibility are evaluated, with the results justifying its efficacy. The study demonstrates a significant improvement of 47.65 % over the baseline, thus highlighting the framework’s superiority.

A Global Medical Data Security and Privacy Preserving Standards Identification Framework for Electronic Healthcare Consumers

A Global Medical Data Security and Privacy Preserving Standards Identification Framework for Electronic Healthcare Consumers

Application of blockchain technology in decentralized medical data security and privacy systems

Application of blockchain technology in decentralized medical data security and privacy systems