Sensitive Patient Information Research Articles

Healthcare Management System Using Blockchain

Abstract: The current healthcare system has traditionally relied on paper-based medical records or electronic medical records (EMRs) that are stored in centralized databases. This method, however, has shown to be ineffective and unreliable, causing major delays in patient care. Medical documents are frequently sent over email as a temporary fix; however, this approach is unsafe and unreliable. Data breaches or unauthorised access to sensitive patient information pose a serious danger and may have far-reaching repercussions for both patients and healthcare providers. Blockchain technology can provide a more secure, decentralized, and efficient way to store and share medical records. Blockchain offers a tamper-proof and auditable method to trace transactions while using cryptographic methods to secure the integrity and confidentiality of data, access to patient data and modifications. Patients, physicians, and hospitals may easily, anytime, anywhere, and with total safety and security, access medical records via a decentralised blockchain network. Patients have discretion over who gets access to their information and can allow healthcare professionals and organisations access to their medical records. We are employing a number of technologies, like MetaMask, Ganache, Web3JS, and Solidity, to develop a blockchain-based solution. Users may connect with blockchain networks via the browser plugin MetaMask, and a local blockchain development tool called Ganache offers a testing environment for blockchain-based apps. Programmers may interface with the Ethereum blockchain using Web3JS, a JavaScript framework, and Solidity, a language used to create smart contracts the blockchain of Ethereum. A centralised blockchain network that can safely store and distribute patient data is the predicted result of a blockchain-based system for medical record sharing. The system's interoperability and scalability may be ensured via integration with already-existing healthcare systems and databases. Medical record sharing may be automated with smart contracts, allowing for safe and auditable access based on predefined criteria and permissions.

Provable Multiple-Copy Integrity Auditing Scheme for Cloud-Based IoT

Cloud-based Internet of Things systems facilitate cloud storage and provide cloud users with convenient data outsourcing service, although there are a number of associated risks to the users (e.g., an untrusted cloud server or their employee/vendor may delete rarely accessed data). In order to ensure that the cloud server stores the data correctly, users need to verify the integrity of remote data periodically. Most of the existing public auditing protocols only considered storing single copy on the cloud; in this case, the loss of users is irreparable when the data are damaged. Subsequently, some multicopy auditing schemes have been proposed where users can store multiple replicas on the cloud. However, it can be challenging to check the integrity of all the copies without incurring significant computation overheads for the auditor/user. Hence, this article proposes a provable multicopy integrity auditing scheme (PMCIA), which reduces the verification cost of the auditor to several lightweight hash operations. Using the medical settings (e.g., where electronic health records containing sensitive information of patients need to be preprocessed before outsourcing storage) as a use case, we explain how our proposed scheme can be deployed. Specifically, to minimize computation overhead for users (e.g., patients), we introduce a hospital information center to help patients generate multiple copies and corresponding tags. We then prove the security under the computational Diffie–Hellman assumption as well as evaluate the performance of our PMCIA scheme. Finally, we explain how PMCIA can be extended to support batch auditing to improve auditing efficiency for multiple auditing tasks.

I Asked a ChatGPT to Write an Editorial About How We Can Incorporate Chatbots Into Neurosurgical Research and Patient Care….

I Asked a ChatGPT to Write an Editorial About How We Can Incorporate Chatbots Into Neurosurgical Research and Patient Care….

SecurePrivChain: A decentralized framework for securing the global model using cryptography

VANETS (IoVs), banks, and healthcare records are the sensitive information of vehicles, clients, and patients that is stored and maintained electronically, which has historically been a popular target for privacy leakage. To address growing issues about privacy leakage in these scenarios, federated learning has been frequently accepted as a privacy-preserving framework that enables multiple users to collectively develop a global model. To preserve the privacy of the training data, the server and participants share only model parameters. However, since participants have access to modified model parameters, this solution is still prone to poisoning and label-flipping attacks. Recent research showed that the federated learning framework is highly susceptible to attacks, with poisoning and label flipping being the most powerful, as well as secluded attacks, in which attackers can poison the parameters of the global model to gain access to the clients’ confidential information or degrade the global model’s performance. To address these attacks, a decentralized framework for securing the global models in federated learning (SecurePrivChain) is proposed in this study. A permissioned private Ethereum blockchain and encryption mechanisms were adopted in the framework to hide global parameters and preserve the privacy of a client’s data as well as achieve client’s authentication. Finally, the proposed model is evaluated using metrics such as computed cost, transaction latency, loss, and cost analysis based on Ethereum gas consumption. The performance of the scheme was compared with other benchmark models which are based on different encryption mechanisms. These included RSA and algamal using publicly available MNIST, banks, vehicles, and hospital datasets. Numerical results show that the proposed framework has good performance. A formal security analysis is done to show how the system can meet its goal of protecting privacy.

ATTRIBUTE-BASED PROXY RE-ENCRYPTION WITH KEYWORD SEARCH ON PERSONAL HEALTH RECORD

Personal health records (PHRs) contain sensitive health information that needs to be protected from unauthorized access. At the same time, patients may need to share their PHRs with healthcare providers or researchers to receive appropriate care or participate in medical studies. Attribute-based proxy re-encryption with keyword search (ABPRE-KS) is a what cryptographic technique that enables secure and controlled sharing of encrypted PHR data while preserving privacy and confidentiality of sensitive patient information. ABPRE-KS works by encryption of the PHR data using a key derived from the patient's attributes and allowing the patient to delegate access to the data to third parties using a proxy re-encryption scheme. To enable keyword search on the encrypted PHR data, an additional searchable encryption scheme can be used. ABPRE-KS provides a powerful tool for securely sharing and searching PHR and it can be used to facilitate better healthcare outcomes and medical research while protecting patient privacy.

Mitigating the consequences of electronic health record data breaches for patients and healthcare workers.

Electronic health records (EHRs) have been widely adopted in Australian public sector healthcare and will remain an ongoing, essential data system. However, recent substantial data breaches from hacked business data systems in Australian enterprises, as well as international healthcare providers, mean that EHR data breaches are increasingly likely in Australia. Risks include medical identity theft and extortion attempts based on threats to release sensitive patient information. Hacking is now a foreseeable additional risk of medical treatment. Risk mitigation for the consequences of data breaches needs to be considered, as well as support for patients (and families) and healthcare workers. This includes identity theft protection services, cybersecurity insurance, and psychological support.

Blockchain in Healthcare: A Decentralized Platform for Digital Health Passport of COVID-19 Based on Vaccination and Immunity Certificates.

COVID-19 has become a very transmissible disease that has had a worldwide impact, resulting in a huge number of infections and fatalities. Testing is critical to the pandemic's successful response because it helps detect illnesses and so attenuate (isolate/cure) them and now vaccination is a life-safer innovation against the pandemic which helps to make the immunity system stronger and fight against this infection. Patient-sensitive information, on the other hand, is now held in a centralized or third-party storage paradigm, according to COVID-19. One of the most difficult aspects of using a centralized storage strategy is maintaining patient privacy and system transparency. The application of blockchain technology to support health initiatives that can minimize the spread of COVID-19 infections in the context of accessibility of the system and for verification of digital passports. Only by combining blockchain technology with advanced cryptographic algorithms can a secure and privacy-preserving solution to COVID-19 be provided. In this article, we investigate the issue and propose a blockchain-based solution incorporating conscience identity, encryption, and decentralized storage via interplanetary file systems (IPFS). For COVID-19 test takers and vaccination takers, our solution includes digital health passports (DHP) as a certification of test or vaccination. We explain smart contracts constructed and tested with Ethereum to preserve a DHP for test and vaccine takers, allowing for a prompt and trustworthy response from the necessary medical authorities. We use an immutable trustworthy blockchain to minimize medical facility response times, relieve the transmission of incorrect information, and stop the illness from spreading via DHP. We give a detailed explanation of the proposed solution's system model, development, and assessment in terms of cost and security. Finally, we put the suggested framework to the test by deploying a smart contract prototype on the Ethereum TESTNET network in a Windows environment. The study's findings revealed that the suggested method is effective and feasible.

BIOMEDICAL IMAGE AUGMENTATION AND TRANSMISSION USING STEGANOGRAPHY

Objective: The aim of the study was to embed sensitive patient information and image data in a cover image to enable secure transmission of confidential data. Methods: Image steganography is a process used to embed (hide with encryption) data which includes text, images or audio, and video files inside the main image file. This embedding is done by altering the values of some pixels which are chosen by the encryption algorithm. The algorithm used in this case is the Discrete Wavelet Transformation Algorithm using MATLAB. Results: Statistical features such as signal to noise ratio (SNR), Peak SNR, and (mean squared error) were extracted from the medical images to test the loss during steganography and transmission. Conclusion: A technique that enables secure and swift image data transmission using image steganography technique is proposed.

KDE-GAN: A multimodal medical image-fusion model based on knowledge distillation and explainable AI modules

BackgroundAs medical images contain sensitive patient information, finding a publicly accessible dataset with patient permission is challenging. Furthermore, few large-scale datasets suitable for training image-fusion models are available. To address this issue, we propose a medical image-fusion model based on knowledge distillation (KD) and an explainable AI module-based generative adversarial network with dual discriminators (KDE-GAN). MethodKD reduces the size of the datasets required for training by refining a complex image-fusion model into a simple model with the same feature-extraction capabilities as the complex model. The images generated by the explainable AI module show whether the discriminator can distinguish true images from false images. When the discriminator precisely judges the image based on the key features, the training can be stopped early, reducing overfitting and the amount of data required for training. ResultsBy training using only small-scale datasets, the trained KDE-GAN can generate clear fused images. KDE-GAN fusion results were evaluated quantitatively using five metrics: spatial frequency, structural similarity, edge information transfer factor, normalized mutual information, and nonlinear correlation information entropy. ConclusionExperimental results show that the fused images generated by KDE-GAN are superior to state-of-the-art methods, both subjectively and objectively.

Enabling Secure Data sharing with data deduplication and sensitive information hiding in cloud-assisted Electronic Medical Systems.

Data sharing is very important for medical researchers to do research on certain diseases in cloud-assisted electronic medical systems. Nonetheless, there are large amounts of duplicate data in shared electronic medical records, which incurs redundant storage. In addition, data sharing of electronic medical records might expose the sensitive information of patients. In order to address above problems, we propose a secure data sharing scheme with data deduplication and sensitive information hiding in cloud-assisted electronic medical systems in this paper. In order to protect the sensitive information privacy and enhance the deduplication efficiency, we replace the patient’s sensitive information of electronic medical records by wildcards before encrypting the whole electronic medical records. The authorized researcher can decrypt and obtain the electronic medical records under the condition that the sensitive information of shared electronic medical records is hidden. Moreover, we clarify the diagnose information of the electronic medical records into different types according to the duplicate ratio. The authorized researchers can selectively download data according to the duplicate ratio of diagnostic information. Our proposed scheme can resist brute-force attacks and single-point-of-failure attack. The experimental results show our proposed scheme is more efficient than the existing schemes.

Health Records Database and Inherent Security Concerns: A Review of the Literature.

The use of electronic health records (EHRs) has grown significantly in the past decade. Health information databases contain sensitive patient information, including their names and addresses, tests, diagnoses, treatment, and medical history. This information should be secured and protected from manipulation and fraudulent use by third parties. EHRs are expected to increase efficiency in healthcare delivery, improve healthcare quality, and relieve increased financial pressure. Despite these expected benefits, EHRs are potentially vulnerable to security concerns that may affect the confidentiality and privacy of patients' personal information. This paper presents a literature review of EHRs, factors that support the security and safety of health records, potential security breaches, and solutions to inherent security concerns. The study collects data through a systematic review of past studies that have addressed the topic of EHRs and security issues, and other relevant publications on EHR systems, and procedures that help safeguard health records databases. A total of 30 sources are analyzed for all pertinent information regarding security concerns of health records databases. These sources were obtained through an internet search on credible databases, including Google Scholar, PubMed, and CINAHL databases. The results of the current study reveal the perceived vulnerability of EHRs to security concerns, common security issues, the nature of these common security concerns, Health Insurance Portability and Accountability Act rules, provider responsibilities, and recommendations for reducing EHR security risks. This paper also reveals effective strategies such as privacy-protection awareness and staff training to enhance the security of health records databases.

A Secure Personal Health Record Sharing System with Key Aggregate Dynamic Searchable Encryption

Recently, as interest in individualized health has increased, the Personal Health Record (PHR) has attracted a lot of attention for prognosis predictions and accurate diagnoses. Cloud servers have been used to manage the PHR system, but privacy concerns are evident since cloud servers process the entire PHR, which contains the sensitive information of patients. In addition, cloud servers centrally manage the PHR system so patients lose direct control over their own PHR and cloud servers can be an attractive target for malicious users. Therefore, ensuring the integrity and privacy of the PHR and allocating authorization to users are important issues. In this paper, we propose a secure PHR sharing system using a blockchain, InterPlanetary File System (IPFS), and smart contract to ensure PHR integrity and secure verification. To guarantee the patient’s authority over the management of his/her own PHR, as well as provide convenient access, we suggest a key aggregate dynamic searchable encryption. We prove the security of the proposed scheme through informal and formal analyses including an Automated Verification of Internet Security Protocols and Applications (AVISPA) simulation, Burrows–Abadi–Needham (BAN) logic, and security-model-based games. Furthermore, we estimate the computational costs of the proposed scheme using a Multiprecision Integer and Rational Arithmetic Cryptographic Library (MIRACL) and compare the results with those of previous works.

An Internet of Things (IoT)-Based Optimization to Enhance Security in Healthcare Applications

The Internet of Things (IoT) is a network that connects a large number of items. Each thing uses sensors to create and gather data from its surroundings and then sends to other objects or a central database through a channel. Keeping and transforming this created data is one of the most difficult tasks in IoT today, and it is one of the top worries of all enterprises that deploy IoT technology. Sensing equipment together with communication, storing, and display devices get benefit from technological advancements in the healthcare sector. Moreover, medical parameters and postoperative days require close observation. Therefore, the most cutting-edge approach to healthcare communication is adopted, which makes use of the Internet of Things (IoT). We can use the Internet of Things to speed changes in the healthcare environment, such as enhancing patient involvement and outcomes and shifting healthcare from reactive to proactive accessibility. Nonetheless, the growth of IoT exposes healthcare practitioners and their patients to new vulnerabilities, risks, and security concerns. However, there is currently a scarcity of research on how to improve IoT security in healthcare. Existing studies tend to concentrate only on the installation of IoT peripherals in a healthcare setting and to include a secure application solution. Because healthcare data and information are extremely sensitive, it is critical to have a secure health IoT application in place. As the IoT gets more widely used in healthcare, there will inevitably be more instances of sensitive patient information being made public. This paper proposes an optimized hashing algorithm with digital certificates to enhance the security. Initially, the health data are collected and preprocessed using normalization. The data are then stored in the IoT device. Here, the digital certificates are used for authentication purpose. The proposed discrete decision tree hashing algorithm (DDTHA) with ant colony optimization (ACO) hashes the unsigned digital certificates. The blowfish algorithm is used for encryption, and the signed digital certificate is obtained which is used for authentication purpose. The performance of the proposed system is evaluated and compared with conventional methodologies to prove the efficiency of the system.

STAR sleep recording export software for automatic export and anonymization of sleep studies

Sleep research often relies on large retrospective clinical datasets. However, as the data is usually stored in proprietary formats specific for each sleep software, the raw data cannot be easily accessed and analyzed with external tools. While the raw data can usually be exported to more common data formats, this is often a cumbersome and labor-intensive task as it is not required for clinical purposes. Additionally, the recordings often include sensitive patient information which must be removed before the data can be shared or analyzed externally. This anonymization can be difficult to perform manually without the correct tools or knowledge of the file types and how they internally store the data. The STAR sleep recording export software provides a simple tool that can be used to perform the sleep study exports automatically. This allows the user to easily export a batch of sleep studies with minimal effort. In addition, the software can also be used to automatically anonymize the exported sleep recordings allowing researchers to save time and personnel resources as these do not need to be allocated for exporting and anonymizing sleep studies. The software supports Noxturnal, RemLogic, Profusion PSG and Sleepware G3 and it is free and openly available for anyone to download and use.

Privacy Enhanced Healthcare Data Management Using Associative Data Mining Approaches

Hospital medical records with health examination findings can be integrated to assist in uncovering the link between aberrant test results and illness. It is possible to establish a disease-preventive knowledge center using these integrated data by performing associated rule mining on the results. In order to integrate data, sensitive patient information must be shared. Patients’ privacy may be violated by the disclosure of sensitive information. Thus, privacy-preserving associated rule mining in physically partitioned healthcare data is addressed in this article. The suggested technique is further evaluated in terms of data protection, transmission, and computing costs.

Techniques of medical image encryption taxonomy

Medical images are one of the most significant and sensitive data types in computer systems. Sending medical images over the internet necessitates using a robust encryption scheme that is resistant to cryptographic attacks. Confidentiality is the most critical part of the three security objectives for information systems security, namely confidentiality, integrity, and availability. Confidentiality is the most critical aspect for the secure storage and transfer of medical images. In this study, we attempt to classify various encryption methods in order to assist researchers in selecting the optimal strategy for protecting sensitive patient information while transferring medical images without alteration and outline the measures that should be adopted to address challenges and concerns relevant to techniques of medical image encryption.

Blockchain and cloud computing-based secure electronic healthcare records storage and sharing.

Healthcare information is essential for both service providers and patients. Further secure sharing and maintenance of Electronic Healthcare Records (EHR) are imperative. EHR systems in healthcare have traditionally relied on a centralized system (e.g., cloud) to exchange health data across healthcare stakeholders, which may expose private and sensitive patient information. EHR has struggled to meet the demands of several stakeholders and systems in terms of safety, isolation, and other regulatory constraints. Blockchain is a distributed, decentralized ledger technology that can provide secured, validated, and immutable data sharing facilities. Blockchain creates a distributed ledger system using techniques of cryptography (hashes) that are consistent and permit actions to be carried out in a distributed manner without needing a centralized authority. Data exploitation is difficult and evident in a blockchain network due to its immutability. We propose an architecture based on blockchain technology that authenticates the user identity using a Proof of Stake (POS) cryptography consensus mechanism and Secure Hash Algorithm (SHA256) to secure EHR sharing among different electronic healthcare systems. An Elliptic Curve Digital Signature Algorithm (ECDSA) is used to verify EHR sensors to assemble and transmit data to cloud infrastructure. Results indicate that the proposed solution performs exceptionally well when compared with existing solutions, which include Proof-Of-Work (POW), Secure Hash Algorithm (SHA-1), and Message Digest (MD5) in terms of power consumption, authenticity, and security of healthcare records.

Open-source distributed learning validation for a larynx cancer survival model following radiotherapy

IntroductionPrediction models are useful to design personalised treatment. However, safe and effective implementation relies on external validation. Retrospective data are available in many institutions, but sharing between institutions can be challenging due to patient data sensitivity and governance or legal barriers. This study validates a larynx cancer survival model performed using distributed learning without any sensitive data leaving the institution. MethodsOpen-source distributed learning software based on a stratified Cox proportional hazard model was developed and used to validate the Egelmeer et al. MAASTRO survival model across two hospitals in two countries. The validation optimised a single scaling parameter multiplied by the original predicted prognostic index. All analyses and figures were based on the distributed system, ensuring no information leakage from the individual centres. All applied software is provided as freeware to facilitate distributed learning in other institutions. Results1745 patients received radiotherapy for larynx cancer in the two centres from Jan 2005 to Dec 2018. Limiting to a maximum of one missing value in the parameters of the survival model reduced the cohort to 1095 patients. The Harrell C-index was 0.74 (CI95%, 0.71–0.76) and 0.70 (0.66–0.75) for the two centres. However, the model needed a scaling update. In addition, it was found that survival predictions of patients undergoing hypofractionation were less precise. ConclusionOpen-source distributed learning software was able to validate, and suggest a minor update to the original survival model without central access to patient sensitive information. Even without the update, the original MAASTRO survival model of Egelmeer et al. performed reasonably well, providing similar results in this validation as in its original validation

Secure Model for IoT Healthcare System under Encrypted Blockchain Framework

With the advancements in the new technologies like IoT, the healthcare sector is also growing rapidly. The core concept of introducing IoT in the healthcare centers is to make it remotely accessible, so that communication between doctor and patient can be easy, and diagnosis of any disease can be remotely done through the Internet in case of emergency. The major objective of this study is to develop a security framework for the healthcare industry that can encrypt secret data on cloud servers using block-based data encoding. Since any information that is available on the Internet is vulnerable to various attacks, and the medical records of the patients carry sensitive information that must not be accessible to any unauthorized or unauthentic person, in this context, the concept of Blockchain (BC) technology was introduced in the healthcare systems. In the past few years, a number of BC based healthcare models were proposed in the system; however, these systems are decentralized in nature, which is a feature of Blockchain technology, but in certain applications, especially in healthcare, this feature gave rise to a problem termed as disease overlapping when the count of chains gets increased. To solve this issue, a BC based framework is used in this work that has a feature of creating a separate block in the chain every time whenever any alteration in information about patient’s health, any allergies, new symptoms, medications, etc. is updated. In addition to this, securing the sensitive information of patients is taken into consideration by developing an improved and enhanced multilayer Blockchain based IoT data security approach that not only protects the data, but also builds the trust between patients/user and healthcare service providers. The main focused area of this research article is to propose a security framework for healthcare domain that can encrypt the confidential data over cloud server under block-based data encoding. In the proposed approach, 128-bit AES key is generated in order to support the SHA-256 hashing algorithm. Individual user’s data is subdivided to blocks and encrypted to secure it from unauthorized access. An interface-based system is designed to present the real-world usage of the proposed model in healthcare application. Finally, the effectiveness of the proposed approach is validated in the MATLAB software in terms of MAE, RMSE, MSE, encryption time, and decryption time. The RSA and DSA are the most trending encryption algorithms. These algorithms are crucial components of Blockchain technology, implemented in the proposed work, and later on, their performances are also compared with each other to check their effectiveness. The simulation results demonstrated that the proposed encrypting algorithm surpasses the other encryption approaches such as RSA and DSA in all factors to prove its supremacy. The experimental results show that the encryption and decryption time are lowest in the proposed framework; therefore, its performance is more effective and robust. It is also more effective and resilient.

Status of bring-your-own-device (BYOD) security practices in Australian hospitals – A national survey

ObjectiveThe use of personal devices for work purposes (BYOD) is in high demand among healthcare professionals as it improves productivity. However, it can also lead to an increased risk of leaking sensitive patient information, being a major challenge for hospital management. The authors identified a dearth of empirical studies concerning BYOD security practices in hospitals. Hence, the aim of the paper was to report BYOD security practices in Australian hospitals through a national survey, first of its kind. MethodsOnline survey was conducted among hospital IT personnel, enquiring about BYOD security practices. 28 responses were collected (21 hospital groups; 7 standalone hospitals), representing approximately 100 public hospitals. Descriptive statistical analysis and cross-tabulation were carried out on survey data. ResultsBYOD was generally allowed across most device types (smartphone:100%; tablet:70%; laptop:70%) and operating systems (apple:100%; android:96%; windows:74%), but generally for the use of basic services such as email (100%). Majority of hospitals use generic security technologies (firewall:87%; two-factor authentication:70%; VPN:65%), whereas BYOD specific technologies (MDM:43%; containerisation:39%; UEM:4%) haven't been widely adopted. Less than half of the surveyed hospitals have a BYOD policy (45%). Staff education though provided in majority of surveyed hospitals (60%), is not comprehensive and regular. Lack of engagement with clinical staff for BYOD strategy development was also found (23%). ConclusionThis research provided a comprehensive view of socio-technical BYOD security practices in Australian hospitals, highlighting its strengths, weaknesses, and the consequential implications, which can therefore provide useful insights for hospital IT executives and health departments.