Privacy Of Medical Information Research Articles

Research on Preventing Medical Information from Leaking based on Homomorphic Encryption

The security and privacy protection of medical information is one of the important challenges facing today's society. Disclosure of medical information can lead to invasion of patient privacy, reputational damage to medical institutions, and potential legal liability. Therefore, it is very important to take effective measures to prevent the leakage of medical information. Information encryption is a common technical means, which can effectively protect the security and privacy of medical information. The purpose of this paper is to discuss how to prevent medical information leakage through information encryption. First, we describe the sensitivity and importance of medical information, as well as the current security challenges. Then, we discuss in detail the basic principles of information encryption and common encryption algorithms, including symmetric encryption and asymmetric encryption. Next, we explore the specific application scenarios of medical information encryption, such as electronic medical records, medical images and encryption protection during transmission. We also discuss the advantages and challenges of healthcare information encryption and propose some solutions such as key management and access control. Finally, we summarize the importance of information encryption in preventing medical information leakage and emphasize the need for further research and practice to ensure the security and privacy protection of medical information.

Legality for Electronic Signatures in Implementing Electronic Medical Records

An electronic signature is an authentic marker to show a person's responsibility. Electronic signatures also ensure confidentiality, integrity, authorization, access control, and non-repudiation of information in electronic medical records. Therefore, this research was conducted to determine the legal aspects of electronic signatures in administering electronic medical records. This research used a qualitative method with a normative juridical approach. The research sources were based on laws, government regulations, journals, and other tertiary sources related to electronic signatures and electronic medical records in health facilities. Research findings illustrated that Minister of Health Regulation Number 24 of 2022 concerning medical records only defined electronic signatures as an authentication and verification of the security and privacy of medical record information. Electronic signatures are specifically regulated by Constitution Number 11 of 2008 concerning Information and Electronic Transactions and Government Regulation Number 71 of 2019 concerning Electronic Systems and Transaction Operators. Both certified and uncertified electronic signatures have legal legitimacy in their application in electronic medical records. However, how they are supported by evidence is what makes them different. Certified electronic signatures are more trustworthy than uncertified electronic signatures.

Somatic rights and medical law: issues of protecting dignity and autonomy

It is noted that somatic rights, which define human rights to the inviolability of the physical body and medical autonomy, are gaining increasing importance in the modern world. The protection and enforcement of these rights are becoming urgent challenges because they determine the way in which health care is provided and received.
 It has been established that somatic rights are defined as human rights to the inviolability of the physical body and the right to control it. These rights include the right to privacy of medical information, the right to freely choose medical procedures, as well as the right to confidentiality and protection from any form of medical discrimination. Somatic rights are an important aspect of general human rights and ensure that individuals can make their own decisions about their physical health and medical procedures.
 It is emphasized that medical law recognizes the right of a person to freedom of choice of medical procedures and treatment, as well as the right to refuse medical intervention. This is related to a person's right to control his physical body and make decisions about it. It also provides protection against any form of medical discrimination based on gender, age, race, religion or other characteristics, which contributes to the observance of the principle of equal opportunities and respect for somatic rights in all areas of medical practice.
 It is emphasized that reproductive rights include the right to freely choose the number of children, intervals between births, as well as the right to access information and services related to reproductive health. They allow a person to make decisions about their reproductive future according to their own values and beliefs. Medical law includes norms and principles that regulate medical practice, relationships between medical professionals and patients, and also ensure compliance with ethical standards in the medical field.

FedEHR: A Federated Learning Approach towards the Prediction of Heart Diseases in IoT-Based Electronic Health Records.

In contemporary healthcare, the prediction and identification of cardiac diseases is crucial. By leveraging the capabilities of Internet of Things (IoT)-enabled devices and Electronic Health Records (EHRs), the healthcare sector can largely benefit to improve patient outcomes by increasing the accuracy of disease prediction. However, protecting data privacy is essential to promote participation and adhere to rules. The suggested methodology combines EHRs with IoT-generated health data to predict heart disease. For its capacity to manage high-dimensional data and choose pertinent features, a soft-margin L1-regularised Support Vector Machine (sSVM) classifier is used. The large-scale sSVM problem is successfully solved using the cluster primal-dual splitting algorithm, which improves computational complexity and scalability. The integration of federated learning provides a cooperative predictive analytics methodology that upholds data privacy. The use of a federated learning framework in this study, with a focus on peer-to-peer applications, is crucial for enabling collaborative predictive modeling while protecting the confidentiality of each participant's private medical information.

Review on E-Health Privacy and Security through ECC, SHA-256, and Multi-Authority Approaches

The rapid digitization of healthcare services has led to the proliferation of electronic health (E-Health) systems, providing convenience and improved patient care. However, the increasing use of E-Health platforms also raises concerns about the privacy and security of sensitive medical information. This review paper explores the challenges surrounding E-Health privacy and security and presents a comprehensive approach to addressing these concerns through the implementation of Elliptic Curve Cryptography (ECC), Secure Hash Algorithm 256 (SHA-256), and multi-authority schemes. The synergistic application of these cryptographic techniques not only fortifies the protection of patient data but also establishes a robust framework for secure data sharing and management in E-Health systems.

Internet of Things for diabetics: Identifying adoption issues

Type 1 diabetes cases are rising globally. Despite being a life-threatening disease, type 1 diabetes can be managed through insulin treatment and glucose monitoring. The use of Internet of Things (IoT) technology, such as insulin pumps, has improved diabetes management and increased patients' quality of life. However, the rapid adoption of IoT in healthcare brings with it potential cybersecurity risks. Recent reports indicate that cyberattacks against the Norwegian healthcare sector have increased 72% in the past year, with an average of 450 attacks weekly. Despite this, many diabetic patients still trust their devices and are willing to accept the risk, perceiving their medical data as not being attractive to cyber criminals.This research aims at exploring the perceptions of various stakeholders on the potential enhancement of life quality for diabetes patients using IoT technology and the challenges associated with privacy and cybersecurity threats in Norway via interviews. The identified stakeholders were divided into three groups, diabetes patients, healthcare personnel and industry representatives. Our preliminary results indicate a lack of concern among both patients and healthcare professionals regarding the privacy of medical information and cybersecurity risks associated with diabetesrelated IoT devices. These findings emphasize the importance of considering the ownership and control of data generated by IoT and the need for a comprehensive review of privacy regulations to ensure uniform interpretation and implementation across all Norwegian healthcare regions. This will enable patients with diabetes to have equal access to technologically advanced medical care and improve their overall quality of life.

For-profit versus non-profit cybersecurity posture: breach types and locations in healthcare organisations.

The implementation of emerging technologies has resulted in an increase of data breaches in healthcare organisations, especially during the COVID-19 pandemic. Health information and cybersecurity managers need to understand if, and to what extent, breach types and locations are associated with their organisation's business type. To investigate if breach type and breach location are associated with business type, and if so, investigate how these factors affect information systems and protected health information in for-profit versus non-profit organisations. The quantitative study was performed using chi-square tests for association and post-hoc comparison of column proportions analysis on an archival data set of reported healthcare data breaches from 2020 to 2022. Data from the Department of Health and Human Services website was retrieved and each organisation classified as for-profit or non-profit. For-profit organisations experienced a significantly higher number of breaches due to theft, and non-profit organisations experienced a significantly higher number of breaches due to unauthorised access. Furthermore, the number of breaches that occurred on laptops and paper/films was significantly higher in for-profit organisations. While the threat level of hacking techniques is the same in for-profit and non-profit organisations, certain breach types are more likely to occur within specific breach locations based on the organisation's business type. To protect the privacy and security of medical information, health information and cybersecurity managers need to align with industry-leading frameworks and controls to prevent specific breach types that occur in specific locations within their environments.

Searchable Encryption With Autonomous Path Delegation Function and Its Application in Healthcare Cloud

Outsourcing medical data to healthcare cloud has become a popular trend. Since medical data of patients contain sensitive personal information, they should be encrypted before outsourcing. However, information retrieval methods based on plaintext cannot be directly applied to encrypted data. In this paper, we present a new cryptographic primitive named conjunctive keyword search with secure channel free and autonomous path delegation function (AP-SCF-PECKS), which can be applied in scenarios where patients want to search for and autonomous delegate their private medical information without revealing their private key. Particularly, the proposed solution allows patients to set up multi-hop delegation path with their preferences, and the delegated doctors in the path can search for and access the patient's private medical information with priority from high to low. Patients can ensure that authorized doctors are always trustworthy, and unauthorized users cannot obtain the private medical information of patients. Moreover, the scheme supports the conjunctive keyword search, secure channel free, and is secure against chosen keyword attack, chosen ciphertext attack, and keyword guessing attack. The security of proposed scheme has been formally proved in the standard model. Finally, the performance evaluations demonstrate that the overhead of proposed scheme are modest for healthcare cloud scenarios.

Security and Data Privacy of Medical Information in Blockchain Using Lightweight Cryptographic System

In the healthcare system and hospital environment, the data security and the data connectivity are the major factors to consider for patient data management system. For that, there are several techniques are used to preserve and arrange the patient data with enhanced security system. In that, Blockchain structure of data management improves the secure data storage and transmission process. In the data security system, the quality measure can be validate by means of the size of key that are used for the encryption process. Combined with the blockchain, the encryption model is to be improved for the solve the problem in data security system. This also needs to focus on the size reduction of data storage due to the large key size of encrypted data. In the proposed work, Intuitionistic Derivative Symmetrical Encryption (IDSE) Algorithm based security algorithm along with blockchain function was integrated to form the authorized data storage and transmission process. For the key pattern generation, Differential Hashing Pattern (DHP) based key pattern extraction model was used for high speed data transmission and to reduce the size of data that are encrypted. The features that are considered for the appropriate security system are the data transmission pattern with respect to the time and the hashing model of key generation. In the result analysis, the performance of the IDSE-DHP are validated with the parameters of data transmission and loss rate in the Blockchain and with the throughput related features.

Saudis' perception of medical information privacy: a point-of-view on the use of medical technology

Saudis' perception of medical information privacy: a point-of-view on the use of medical technology

Enhancing Healthcare Monitoring through the Integration of IoT Networks and Machine Learning

The technology that was developed during the fourth industrial revolution has contributed to the recent surge of interest that has been seen in the field of medicine. In particular, the importance of personal medical information obtained via knowledgeable self-diagnosis is becoming more apparent. However, the disclosure of such private medical information raises several concerns regarding trustworthiness and security. Accidents involving personally identifiable medical information could happen on the computer, but more frequently than not, they take place during the process of information exchange and data transfer. So, the goal of this research is to improve the trustworthiness of managing such sensitive data by making blockchain technology better. The objective of the project was to create smart healthcare systems by utilising blockchain technology and the Internet of Things (IoT). Moreover, they utilised various measuring instruments to collect data and carry out an individual electrocardiogram assessment. Through an examination of the fused threshold, the observed biosignals were analysed to provide a tailored diagnostic. In this article, we describe the implementation of a monitoring system that analyses individual biometric information by making use of measuring devices. Machine learning has been included in the deployed system, which has resulted in better dependability and security of the system's information.

A novel system for medical equipment supply chain traceability based on alliance chain and attribute and role access control

With the increasing sales of the medical device market, the medical incidents caused by passive medical devices are increasing, which needs to be solved from the medical device supply chain. In addition, there are complex rights retrieval and assignment problems in the medical device supply chain system, and the traditional Role-based Access Control (RBAC) model is no longer suitable for the actual application environment of the supply chain.This article combines the traditional RBAC model and the Attribute-based Access Control (ABAC) model for access control management in the medical device supply chain to overcome the limitations abovementioned, taking advantage of the ABAC model’s flexibility to achieve granular and dynamic management of permissions, as also the RBAC model to simplify the permissions management of the entire system. Blockchain technology’s traceability, non-tampering, and decentralization features are essential to eliminate passive and inferior medical equipment sales activities, solving the problem of mutual mistrust and ‘Information Silo’ between the two sides of medical device transactions. The access control model is implemented in the blockchain’s smart contract, providing granular and dynamic authority management for the medical equipment supply chain and guaranteeing the security and privacy of medical device information in the supply chain. Three smart contracts on Hyperledger Fabric are designed for device management information, storage of implemented access control models, and implementation of access control policies. Experimental results show that the entire system is stable, ensuring high throughput and high security and promising.

What Should a Firm Know? Protecting Consumers’ Privacy Rents

A monopolistic firm observes a signal about the state of the world and then makes a take-it-or-leave-it offer to an uninformed consumer who has recourse to some outside option. We provide a geometric characterization of the firm’s information structure that maximizes the consumer’s surplus: the optimal regime partitions the space of payoff states into polyhedral cones with disjoint interiors. We interpret our results in terms of the maximization of the consumer’s “privacy rent.” We illustrate and motivate our approach through the example of the regulation of the privacy of medical information in monopolistic health insurance markets. (JEL D21, D42, D83, G22, I13)

Cyber Physical System Based Smart Healthcare System with Federated Deep Learning Architectures with Data Analytics

Data shared between hospitals and patients using mobile and wearable Internet of Medical Things (IoMT) devices raises privacy concerns due to the methods used in training. the development of the Internet of Medical Things (IoMT) and related technologies and the most current advances in these areas The Internet of Medical Things and other recent technological advancements have transformed the traditional healthcare system into a smart one. improvement in computing power and the spread of information have transformed the healthcare system into a high-tech, data-driven operation. On the other hand, mobile and wearable IoMT devices present privacy concerns regarding the data transmitted between hospitals and end users because of the way in which artificial intelligence is trained (AI-centralized). In terms of machine learning (AI-centralized). Devices connected to the IoMT network transmit highly confidential information that could be intercepted by adversaries. Due to the portability of electronic health record data for clinical research made possible by medical cyber-physical systems, the rate at which new scientific discoveries can be made has increased. While AI helps improve medical informatics, the current methods of centralised data training and insecure data storage management risk exposing private medical information to unapproved foreign organisations. New avenues for protecting users' privacy in IoMT without requiring access to their data have been opened by the federated learning (FL) distributive AI paradigm. FL safeguards user privacy by concealing all but gradients during training. DeepFed is a novel Federated Deep Learning approach presented in this research for the purpose of detecting cyber threats to intelligent healthcare CPSs.

PPSEB: A Postquantum Public-Key Searchable Encryption Scheme on Blockchain for E-Healthcare Scenarios

In the current E-healthcare scenarios, medical institutions are used to encrypt the information and store it in an Electronic Health Record (EHR) system in order to ensure the privacy of medical information. To realize data sharing, a Public-key Encryption with Keyword Search (PEKS) scheme is indispensable, ensuring doctors search for medical information in the state of ciphertext. However, the traditional PEKS scheme cannot resist the keyword guessing quantum computing attacks, and its security depends on the confidentiality of the secret key. In addition, classical PEKS hand over the search process to a third party, affecting the search results’ accuracy. Therefore, we proposed a postquantum Public-key Searchable Encryption scheme on Blockchain (PPSEB) for E-healthcare scenarios. Firstly, we utilized a lattice-based cryptographic primitive to ensure the security of the search process and achieve forward security to avoid key leakage of medical information. Secondly, we introduced blockchain technology to solve the problem of third-party untrustworthiness in the search process. Finally, through security analysis, we prove the correctness and forward security of the solution in the E-healthcare scenarios, and the comprehensive performance evaluation demonstrates the efficiency of our scheme compared with other existing schemes.

Exploring users' willingness to disclose personal information in online healthcare communities: The role of satisfaction

To save time and reduce treatment costs, an increasing number of people are seeking medical services from online healthcare platforms. However, individuals face many threats when using online healthcare services, such as leaking private medical information. This study focuses on users' privacy concerns on how their satisfaction affects their willingness to disclose personal information. The study explores the relationships amongst the factors, the security control of the platform, perceived usefulness, information sensitivity, privacy concerns in platforms, patients' satisfaction, trust, and disclosure intention using a survey approach. The results indicate that concerns about online healthcare platforms negatively influence satisfaction. Patients' trust in the platform positively influences their willingness to disclose by improving their satisfaction. Satisfaction is key to improving patients' disclosure intention. The findings offer a better understanding of patients' information disclosure intention from the perspective of satisfaction and help online healthcare communities to strengthen the doctor–patient relationship.

Patient privacy in smart cities by blockchain technology and feature selection with Harris Hawks Optimization (HHO) algorithm and machine learning.

A medical center in the smart cities of the future needs data security and confidentiality to treat patients accurately. One mechanism for sending medical data is to send information to other medical centers without preserving confidentiality. This method is not impressive because in treating people, the privacy of medical information is a principle. In the proposed framework, the opinion of experts from other medical centers for the treatment of patients is received and consider the best therapy. The proposed method has two layers. In the first layer, data transmission uses blockchain. In the second layer, blocks related to patients’ records analyze by machine learning methods. Patient records place in a block of the blockchain. Block of patient sends to other medical centers. Each treatment center can recommend the proposed type of treatment and blockchain attachment and send it to all nodes and treatment centers. Each medical center receiving data of the patients, then predicts the treatment using data mining methods. Sending medical data between medical centers with blockchain and maintaining confidentiality is one of the innovations of this article. The proposed method is a binary version of the HHO algorithm for feature selection. Another innovation of this research is the use of majority voting learning in diagnosing the type of disease in medical centers. Implementation of the proposed system shows that the blockchain preserves data confidentiality of about 100%. The reliability and reliability of the proposed framework are much higher than the centralized method. The result shows that the accuracy, sensitivity, and precision of the proposed method for diagnosing heart disease are 92.75%, 92.15%, and 95.69%, respectively. The proposed method has a lower error in diagnosing heart disease from ANN, SVM, DT, RF, AdaBoost, and BN.

An Efficient Online Multiparty Interactive Medical Prediagnosis Scheme with Privacy Protection

Medical prediagnosis systems are now available online to give users quick and preliminary diagnosis information. The need for such a system has become particularly evident in areas with insufficient health professionals. Due to the privacy of patient medical information and the sensitivity of cloud diagnosis models, it is necessary to protect the security of data, models, and communications. These existing diagnosis systems can hardly provide a satisfied diagnosis accuracy while ensuring comprehensive security and high efficiency. In order to solve these problems, we proposed Relief- k minimum Wasserstein distance (Relief- k MW) classification method, which combined data encryption and BLS signature to form a privacy-preserving efficient online multiparty interactive medical prediagnostic scheme (OMPD). Theoretical analysis shows our OMPD effectively provides high-precision prediagnosis services. Extensive experimental results demonstrate that OMPD not only greatly improves the diagnostic accuracy but also reduces the computational and communication overhead.

Lagrange interpolation-driven access control mechanism: Towards secure and privacy-preserving fusion of personal health records

A personal health record (PHR) contains health information on a specific individual and incorporates medical data from cloud databases (e.g., diagnosis and treatment records). Because of the use of medical applications on smart mobile devices, security mechanisms must be implemented. Secure access control enables convenient information sharing under secure conditions. To effectively implement secure access control for PHRs in cloud databases, we propose a Lagrange-interpolation-driven access control mechanism (LIDACM) that ensures the security and confidentiality of healthcare information. The LIDACM strictly controls privacy settings and access authority; it also prevents unauthorised individuals from accessing PHRs, thereby increasing the difficulty of hacking the database and stealing private medical information. The present analysis indicates that the LIDACM effectively protects PHR information and enables secure information sharing among multiple medical institutions. Because a user’s private key is generated randomly through the proposed mechanism and no relationships exist between private keys, cracking the private key system is difficult. The LIDACM supports dynamic modifications (e.g., user addition and deletion and document or user inquiry) and can deliver files to specific users when they use a private key. Each user has specific access rights pertaining to retrieval of confidential data. The LIDACM helps guarantee the privacy and security of personal medical information within an information-sharing system.

FoG-Oriented Secure and Lightweight Data Aggregation in IoMT

Internet of Medical Things (IoMT) is becoming an essential part of remote health monitoring due to the evolution of medical wireless sensors and intelligent communication technologies. IoT-based healthcare applications are employed in the medical centers to provide continuous health monitoring of a patient. However, recent smart medical devices have limited resources to handle the huge amount of healthcare data. IoMT faces several challenging issues, like security, privacy, anonymity, and interoperability. In data aggregation and communication, the privacy and security of medical information is a demanding task. Therefore, we proposed a suitable scheme to overcome the limitations of existing research studies. This paper presents an Efficient and Secure Data Transmission and Aggregation (ESDTA) scheme to enhance aggregation efficiency and data security. Our proposed work provides secure data aggregation and data forwarding of healthcare parameter values by employing the Secure Message Aggregation (SMA) algorithm and Secure Message Decryption (SMD) algorithm at the Mobile Node (MN) and Fog Node (FN), respectively. From a security perspective, the proposed scheme preserves the data integrity and also protect against several security threats like data fabrication and replay attack. The proposed scenario is simulated through simulation tool NS 2.35. The simulation results prove that aggregation at the MN effectively reduces transmission and communication costs. Furthermore, the effective computation at the FN minimizes the storage and computational cost at the cloud server. Thus, the analysis of the proposed scheme shows the supremacy of our proposed work. We compare our scheme with other related secure data aggregation-based schemes in terms of communication cost, energy consumption, resilience, storage and computational cost.