Privacy Of Medical Data Research Articles

Private and Secure Medical Data Transmission Using Wireless Network with QR Code

Abstract: In medical management, more and more information technology is applied to improve work efficiency. For example, the hospital information management system is used for basic information and medical management of patients, and the onedimensional and two-dimensional codes on the wrist are used for quick reading or inputting patient identification (ID), etc. Although computerization is more convenient, due to immature technology or management flaws, there are certain security risks in various typical scenarios, such as disclosure of user privacy through reporting transparency, lack of strict control over the consultation of confidential medical records, the absence of technical verification for the confirmation of the infusion, etc. Identity is easy to fake, payment is inconvenient, etc. Security issues are discussed in more detail below. Healthcare applications are considered a promising area for wireless sensor networks, where patients can be monitored using wireless medical networks (WMN). Current trends in WMN healthcare research focus on reliable patient communication, patient mobility, and energy-efficient delivery.

Lightweight certificateless privacy-preserving integrity verification with conditional anonymity for cloud-assisted medical cyber–physical systems

Cloud-assisted medical cyber–physical systems (MCPSs) leverage the power of cloud computing for scalable storage and management of outsourced medical data. Based on the crucial outsourced medical data, guardians could know patient’s health condition timely, doctors can make accurate diagnoses of patients, and medical research centers could also conduct medical big data analysis. However, medical data is highly sensitive and demands strong protection. Meanwhile, the integrity of outsourced medical data is vulnerable to hardware failures, software bugs or human errors. To date, a large number of data privacy-preserving integrity auditing schemes have been presented, but most of them suffer from the burden of complex public key certificate management or the key escrow problem of identity-based cryptography. In this paper, we propose a lightweight certificateless privacy-preserving integrity verification scheme (LCPPIV) with conditional anonymity for cloud-assisted MCPSs by developing an elliptic-curve digital signature and a key exchange mechanism, which can achieve the functionalities of medical data privacy protection, integrity verification of outsourced medical data, conditional anonymity, batch auditing, and secure compensation mechanism, simultaneously. Theoretical security analysis demonstrates that our scheme is provably secure in the random oracle model, with resistance to the public key replacement attack, malicious-but-passive-KGC attack, and response auditing proofs forgery. The performance evaluations indicate that LCPPIV is much more practical for cloud-assisted MCPSs compared with state-of-art data auditing schemes.

A Novel Optimized Perturbation-Based Machine Learning for Preserving Privacy in Medical Data.

In recent times, providing privacy to the medical dataset has been the biggest issue in medical applications. Since, in hospitals, the patient's data are stored in files, the files must be secured properly. Thus, different machine learning models were developed to overcome data privacy issues. But, those models faced some problems in providing privacy to medical data. Therefore, a novel model named Honey pot-based Modular Neural System (HbMNS) was designed in this paper. Here, the performance of the proposed design is validated with disease classification. Also, the perturbation function and the verification module are incorporated into the designed HbMNS model to provide data privacy. The presented model is implemented in a python environment. Moreover, the system outcomes are estimated before and after fixing the perturbation function. A DoS attack is launched in the system to validate the method. At last, a comparative assessment is made between executed models with other models. From the comparison, it is verified that the presented model achieved better outcomes than others.

Blockchain and Artificial Intelligence-based Solutions for Healthcare Management: Liver Disease Detection as a Case Study

The issue of privacy in clinical data restricts data sharing among various organizations because of legal and ethical concerns. Every medical organization (hospital, research center, testing lab, etc.) needs to protect personal and medical data privacy and confidentiality while also sharing data with efficient and accurate learning models for various diseases. This paper addresses a method that combines machine learning approaches on blockchain network for classification of patients with liver disease or not, while maintaining the privacy and enabling data sharing. The contribution of this paper is five-folds: (i) To deal with the liver disease dataset, data preprocessing phases are utilized (i.e., missing data treatment, data normalization, and data transformation). (ii) For feature reduction, two feature selection models are used: correlation feature selection (CFS) and wrapper metaheuristic (PSO+KNN). For training the proposed global model, the selected features (10, 4, 3) from the three models (all features, CFS, PSO+KNN) are distributed to the shared blockchain network. (iii) To detect liver disease in the preprocessed dataset, three machine learning methods are proposed: random forest, decision tree (J48), and Naïve Bayes. (iv) The distributed network (blockchain) will publish the results of the liver disease diagnosis on the network. (v) The smart contract shares the data and diagnosis between the medical organizations for future processing. An empirical study is executed to validate the relevance of our suggested framework for early diagnosis of liver disease using blockchain and machine learning models. The experimental results showed that feature selection using the wrapper metaheuristic (PSO+KNN) with a random forest model achieves the best performance with the minor features for detecting liver disease with an accuracy of 99.9 compared to 99.2, 89.2 for Naïve Bayes, and J48, respectively.

Privacy-Preserving Multi-Source Domain Adaptation for Medical Data.

Great progress has been made in diagnosing medical diseases based on deep learning. Large-scale medical data are expected to improve deep learning performance further. It is almost impossible for a single institution to collect so much data due to the time-consuming and costly collection and labeling of medical data. Many studies have turned attention to data sharing among multiple medical institutions. However, due to different data acquiring and processing procedures, multiple institutions' medical data is characterized by distribution heterogeneity. Besides, the protection of patient privacy in medical data sharing has also been a common concern. To simultaneously address the problems of heterogeneous data distribution and privacy protection, we propose a novel multi-source source free domain adaptation. When aligning distributed heterogeneous data, our method only require to transfer the pre-trained source models rather than the direct source domain data, thus protecting patients' privacy. In addition, it has the advantages of being efficient and less costly in network resources. The proposed method is evaluated on the multi-site fMRI database Autism Brain Imaging Data Exchange (ABIDE) and yields an average accuracy of 69.37%. We also analyzed its effectiveness on network resource-saving and conducted additional experiments on Camelyon17 to validate the generalization.

Federated Learning-Based Secure Electronic Health Record Sharing Scheme in Medical Informatics.

Medical Cyber-Physical Systems support the mobility of electronic health records data for clinical research to accelerate new scientific discoveries. Artificial Intelligence improves medical informatics, but current centralized data training and insecure data storage management techniques expose private medical data to unauthorized foreign entities. In this paper, a Federated Learning-based Electronic Health Record sharing scheme is proposed for Medical Informatics to preserve patient data privacy. A decentralized Federated Learning-based Convolutional Neural Network model trains data locally in the hospital and stores results in a private InterPlanetary File System. A secondary global model is trained at the research center using the local models. Private IPFS secures all medical data stored locally in the hospital. The novelty of this study resides in securing valuable hospital biomedical data useful for clinical research organizations. Blockchain and smart contracts enable patients to negotiate with external entities for rewards in exchange for their data. Evaluation results demonstrate that the decentralized CNN model performs better in accuracy, sensitivity, and specificity, similar to the traditional centralized model. The performance of the Private IPFS exceeds the Blockchain-based IPFS based on file upload and download time. The scheme is suitable for promoting a secure and privacy-friendly environment for sharing data with clinical research centers for biomedical research.

Blockchain Technology: Security Issues, Healthcare Applications, Challenges and Future Trends

Blockchain technology provides a data structure with inherent security properties that include cryptography, decentralization, and consensus, which ensure trust in transactions. It covers widely applicable usages, such as in intelligent manufacturing, finance, the Internet of things (IoT), medicine and health, and many different areas, especially in medical health data security and privacy protection areas. Its natural attributes, such as contracts and consensus mechanisms, have leading-edge advantages in protecting data confidentiality, integrity, and availability. The security issues are gradually revealed with in-depth research and vigorous development. Unlike traditional paper storage methods, modern medical records are stored electronically. Blockchain technology provided a decentralized solution to the trust-less issues between distrusting parties without third-party guarantees, but the “trust-less” security through technology was easily misunderstood and hindered the security differences between public and private blockchains appropriately. The mentioned advantages and disadvantages motivated us to provide an advancement and comprehensive study regarding the applicability of blockchain technology. This paper focuses on the healthcare security issues in blockchain and sorts out the security risks in six layers of blockchain technology by comparing and analyzing existing security measures. It also explores and defines the different security attacks and challenges when applying blockchain technology, which promotes theoretical research and robust security protocol development in the current and future distributed work environment.

A Privacy Protection Framework for Medical Image Security without Key Dependency Based on Visual Cryptography and Trusted Computing

The development of mobile Internet and the popularization of intelligent sensor devices greatly facilitate the generation and transmission of massive multimedia data including medical images and pathological models on the open network. The popularity of artificial intelligence (AI) technologies has greatly improved the efficiency of medical image recognition and diagnosis. However, it also poses new challenges to the security and privacy of medical data. The leakage of medical images related to users’ privacy is emerging one after another. The existing privacy protection methods based on cryptography or watermarking often bring a burden to image transmission. In this paper, we propose a privacy‐preserving recognition network for medical images (called MPVCNet) to solve these problems. MPVCNet uses visual cryptography (VC) to transmit images by sharing. Benefiting from the secret‐sharing characteristics of VC, MPVCNet can securely transmit images in clear text, which can both protect privacy and mitigate performance loss. Aiming at the problem that VC is easy to forge, we combine trusted computing environments (TEE) and blind watermarking technologies to embed verification information into sharing images. We further leverage the transfer learning technology to abate the side effect resulting from the use of visual cryptography. The results of the experiment show that our approach can maintain the trustworthiness and recognition performance of the recognition networks while protecting the privacy of medical images.

An advanced cloud based framework for privacy and security in medical data using cryptographic method

The exchange of medical information has been drastically altered by patient-centered developments such as personal health records (PHR). By giving patients a place to handle their own PHR on a unified transactional platform, personal health record (PHR) services increase the efficiency with which medical information may be kept, accessed, and transferred. With the ultimate objective of providing patients with total surveillance under data, our findings is focused on creating a state-of-the-art infrastructure for the safe transfer of personal health data via cloud computing. Patients have the option of encrypting their PHR files, which provides an additional layer of security and allows them to set access control limits such as who has access to their files and to what degree. When data is encrypted in the cloud, only approved users may access it. Using cloud-based platforms to share health records raises concerns over confidentiality and privacy, which are addressed by the proposed method. Patients may still benefit from data interchange for the goal of better healthcare thanks to the framework’s provision of an encrypted PHR file option. This framework may accommodate attribute-based encryption (ABE) and other kinds of granular security. These measures ensure that people may continue to have access to, and make changes to, their own medical data, even when they are stored on the cloud. This article presents research that attempts to meet the demands of patients while also providing a safe method of transferring individual health information through cloud computing.

An Improved Steganographic Scheme Using the Contour Principle to Ensure the Privacy of Medical Data on Digital Images

With the improvement of current online communication schemes, it is now possible to successfully distribute and transport secured digital Content via the communication channel at a faster transmission rate. Traditional steganography and cryptography concepts are used to achieve the goal of concealing secret Content on a media and encrypting it before transmission. Both of the techniques mentioned above aid in the confidentiality of feature content. The proposed approach concerns secret content embodiment in selected pixels on digital image layers such as Red, Green, and Blue. The private Content originated from a medical client and was forwarded to a medical practitioner on the server end through the internet. The K-Means clustering principle uses the contouring approach to frame the pixel clusters on the image layers. The content embodiment procedure is performed on the selected pixel groups of all layers of the image using the Least Significant Bit (LSB) substitution technique to build the secret Content embedded image known as the stego image, which is subsequently transmitted across the internet medium to the server end. The experimental results are computed using the inputs from “Open-Access Medical Image Repositories (aylward.org)” and demonstrate the scheme’s impudence as the Content concealing procedure progresses.

A Review of Commercial and Non-Commercial Wearables Devices for Monitoring Motor Impairments Caused by Neurodegenerative Diseases

Neurodegenerative diseases (NDDs) are among the 10 causes of death worldwide. The effects of NDDs, including irreversible motor impairments, have an impact not only on patients themselves but also on their families and social environments. One strategy to mitigate the pain of NDDs is to early identify and remotely monitor related motor impairments using wearable devices. Technological progress has contributed to reducing the hardware complexity of mobile devices while simultaneously improving their efficiency in terms of data collection and processing and energy consumption. However, perhaps the greatest challenges of current mobile devices are to successfully manage the security and privacy of patient medical data and maintain reasonable costs with respect to the traditional patient consultation scheme. In this work, we conclude: (1) Falls are most monitored for Parkinson's disease, while tremors predominate in epilepsy and Alzheimer's disease. These findings will provide guidance for wearable device manufacturers to strengthen areas of opportunity that need to be addressed, and (2) Of the total universe of commercial wearables devices that are available on the market, only a few have FDA approval, which means that there is a large number of devices that do not safeguard the integrity of the users who use them.

The evolution of private medical expenses for civil servants in Germany

In this study we analyse private medical insurance data with respect to civil servants in Germany. This data is applied to pricing purposes in the market and may therefore be deemed as useful. In particular, we discuss average health care expenditure per capita by age over time. Thereby, we analyse inpatient and outpatient benefits as well as hospital cash and dental insurance. In essence, we observe an effect that we call the rectangularisation of the cumulative health care expenditure curve. In line with this finding, we note that the modal age (at which total medical expenses for in force business are highest) has increased by 10 years of age over the last decade with respect to inpatient benefits. Moreover, we apply a linear regression model based on mortality rates as regressor. We use the Siegel test for comparing different expenditure profiles and judging goodness of fit. As a result, we find that steepening of private medical expenses by age over time is prevalent for three out of four benefit types in certain age bands. As for outpatient claims provided to male civil servants, medicalisation dominates the evolution of the average claim amount by attained age over time. On the other hand, compression is of relatively high importance for inpatient claims.

Modeling Asynchronous Federated Learning for Health care application

Healthcare data from clinical institution and pharmaceutical industries are fragmented and are sensitive with protected health information of individual. Data privacy, safety and security are of prime important. It is vital to train machine learning model without compromising privacy. In federated learning the global model is aggregated on aggregation servers as per the parameter of local model instead of local data. There is waiting for straggler client before aggregation in each round .Every client operated at its own speed of training. Asynchronous federated learning aggregates the global model based on available updated model received at the server asynchronously during each round of training. Asynchronous federated learning improves the efficiency, performance, privacy and security. In this paper, based on the Lung cancer dataset, the classification model is built. With NVIDIA flare, synchronous federated learning is carried out with one server and three clients for spliited data of lung cancer of Kaggle. The performance of synchronous federated learning is observed similar to the classical machine learning model. Modeling of asynchronous federated learning is carried out with Petri Net and analyzed for the reachability analysis with workcraft software. Substantial improvement in medical data privacy, security and safety is observed.

WOGRU-IDS — An intelligent intrusion detection system for IoT assisted Wireless Sensor Networks

One of the key mechanisms of the current electronic and wireless frameworks is the assistance of Wireless Sensor Networks (WSN) in Internet of Things (IoT) networks. A WSN typically consists of multipurpose sensor hubs for data sensing, processing, and communication. These networks are more suited to conveying medical data from various geographical regions and sending private medical data to the network owner. However, the worry about various attacks on health care data normally grows daily. These assaults could quickly have adverse impacts on the WSN-IoT (Internet of Things) nodes. Additionally, the low detection rate, significant processing overhead, and resource limitations of current intrusion detection systems all contribute to an increase in false alarm rates when trying to identify various attacks. The unique Whale Optimized Gate Recurrent Unit (WOGRU) Intrusion Detection System (IDS) for WSN-IoT networks is proposed in this research in light of the aforementioned issues in order to effectively identify various attacks. The whale algorithm was used in the proposed framework to tune the hyperparameters of the deep long short-term memory in order to achieve low computational overhead and great performance. Last but not least, validations are carried out using the WSN-DS dataset, and the performance of the suggested work is evaluated using the parameters accuracy, recall, precision, specificity, and F1-score. Additionally, the comparison study was conducted using the current frameworks. The data demonstrates that the suggested framework had an average performance of 99.85 percent for the detection of flooding, scheduling, black hole, and gray hole attacks.

A User-Centered Medical Data Sharing Scheme for Privacy-Preserving Machine Learning

With the rapid development and application of artificial intelligence technology, medical data play an increasingly important role in the medical field. However, there are privacy protection and data ownership issues in the process of data sharing, which brings difficulties to machine learning and data mining. On the one hand, for fear that they may risk being held accountable by users or even breaking the law due to these issues, healthcare providers are reluctant to share medical data. On the other hand, users are also reluctant to share medical data due to the possibility of privacy disclosure in the data sharing process. To improve the security and privacy of shared medical data, we propose a user-centered medical data sharing scheme for privacy-preserving machine learning. Our solution combines blockchain and a trusted execution environment to ensure that adversaries cannot steal the ownership and control of user data during sharing. A blockchain-based noninteractive key sharing scheme is proposed that allows only the users and the TEE to decrypt the shared data. At the same time, we design an auditing mechanism to facilitate users to audit the sharing process. The security analysis shows that the scheme ensures the privacy and security of user data during storage and sharing. We have completed simulation experiments to demonstrate the effectiveness and efficiency of our scheme.

A robust medical image steganography approach based on particle swarm optimization algorithm and quantum walks

Medical information plays an essential task in our everyday lives, in which medical data privacy and security constitute an important issue. The confidentiality of medical data can be achieved by applying one or more encryption and data hiding methods. Amidst the development of quantum computers, most medical data confidentiality techniques may be hacked because their construction is based on mathematical models. Most medical data have a long lifetime exceeding 25 years. Therefore, it is an important issue to design a new medical data hiding technique that has the capability to withstand the probable attacks from the side of quantum or digital devices. In this article, we aim to present a novel medical image steganography strategy based on quantum walks, chaotic systems, and particle swarm optimization algorithm. A 3-D chaotic system and quantum walks are utilized for operating particle swarm optimization algorithm, in which the generated velocity sequence is utilized for substituting the confidential data, and the position sequence is utilized for selecting which position in the hosting image will be employed to host the substituted confidential data. The payload capacity of the suggested mechanism is 2 bits per 1 byte, and the average value for PSNR is 44.1, which is big enough for the naked eye to not differentiate the difference between the carrier image and its stego one.

Research and design of medical data sharing and insurance automatic claim system based on blockchain technology

In recent years, blockchain technology has been applied to various business scenarios with its characteristics of decentralization, openness, self-consistency and information immutable. In the medical insurance industry, disputes often occur between insurance institutions and policyholders due to inaccurate personal information and different confirmation of contract terms. With the digitalization of medical information and the rapid development of science and technology, insurance claim settlement schemes based on blockchain and homomorphic encryption can enable the hospital, the insurance institution and policyholders to establish a trusted mechanism, which can ensure the authenticity and security of the policyholder's personal information and the validity of the contract. The scheme combines Paillier homomorphic encryption and blockchain smart contract while designing access control list to control patients' data. In the scenario of insurance claim settlement, it can not only protect the privacy of patients' medical data, but also ensure the correct settlement result of insurance claim.

Decentralized authentication and access control protocol for blockchain-based e-health systems

The Internet of things technology has accelerated the development of e-health solutions and allows patients to obtain a more satisfying medical service experience. Each electronic health system(EHS) usually allows its subsystems to manage and access patient data to achieve its unique objective. Due to the limited computing power and storage capacity of the terminal, it is difficult to protect large amounts of patients’ medical record data in EHS. Medical data are a meaningful medical history resource, and are very important for the treatment and rehabilitation of patients. The privacy and integrity protection of medical data is a on-negotiable requirement for rational application in traditional medical systems. To achieve secure resource sharing and efficient service provision between users and medical service providers, we present a blockchain-based decentralized authentication and access control protocol for EHS that addresses broader medical data privacy and security needs. A practical Byzantine fault-tolerant(PBFT) negotiation mechanism based on multi-weighted subjective logic is adopted to improve the negotiation process. Finally, using BAN logic, we prove the reliability of the systems-security protections, which demonstrates that our proposal can withstand known security attacks.

DeepKeyGen: A Deep Learning-Based Stream Cipher Generator for Medical Image Encryption and Decryption.

The need for medical image encryption is increasingly pronounced, for example, to safeguard the privacy of the patients' medical imaging data. In this article, a novel deep learning-based key generation network (DeepKeyGen) is proposed as a stream cipher generator to generate the private key, which can then be used for encrypting and decrypting of medical images. In DeepKeyGen, the generative adversarial network (GAN) is adopted as the learning network to generate the private key. Furthermore, the transformation domain (that represents the "style" of the private key to be generated) is designed to guide the learning network to realize the private key generation process. The goal of DeepKeyGen is to learn the mapping relationship of how to transfer the initial image to the private key. We evaluate DeepKeyGen using three data sets, namely, the Montgomery County chest X-ray data set, the Ultrasonic Brachial Plexus data set, and the BraTS18 data set. The evaluation findings and security analysis show that the proposed key generation network can achieve a high-level security in generating the private key.

Medical Data Storage Model Based on an Alliance Chain

Aiming at the problems of centralized storage, low sharing efficiency, and the security and privacy of traditional medical data, a medical data storage model based on a consortium chain is proposed. First, the Distance algorithm is designed based on the geographical relationship of nodes, which reduces the amount of communication between nodes, improves the communication efficiency between A nodes, and ensures the efficiency and reliability of grouping. Second, the dynamic election is combined with the Distance algorithm to design the election strategy of the primary node, which realizes the scientific and reasonable ranking of the medical nodes, improves the reliability of the selection of the primary node, and ensures the efficiency of the medical nodes in the alliance chain to quickly reach consensus when the medical data are uploaded to the chain. Finally, the model designs a method of information separation, which combines the distributed database IPFS (InterPlanetary File System) with the blockchain, which greatly reduces the pressure of medical data storage in the blockchain and improves the operation efficiency. The experimental results show that the medical data storage model can effectively improve the problem of the sharp increase in the amount of communication between medical nodes in the network and reduce the impact of malicious consensus nodes.