Privacy-Preserving Infrastructure for Health Information Systems

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

Health Systems Need to Transform Data Collection to Advance Health Equity.

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

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.

Venous thromboembolism (VTE) is a common vascular disorder requiring extended anticoagulation therapy postdischarge to reduce recurrence risk. Home rehabilitation management systems that use electronic health records from hospital care provide opportunities for continuous patient monitoring. However, transferring medical data from clinical to home settings raises significant concerns about privacy and security. Conventional methods such as manual data entry, optical character recognition, and dedicated data transmission lines face notable technical and operational challenges. This study aims to develop a QR code-based security transmission algorithm using Avro and byte pair encoding (BPE). The algorithm supports the secure creation and transfer of out-of-hospital health records by enabling patients to scan QR codes via a dedicated mobile app, ensuring data security and user privacy. Between January and October 2024, 300 hospitalized patients with VTE were recruited at the Sixth Medical Center of the Chinese PLA General Hospital. Post discharge, participants used a home rehabilitation app tailored for VTE management. The QR code-based security transmission algorithm was developed to securely transfer in-hospital electronic health records to the out-of-hospital app. It uses BPE, Avro, and Gzip for optimized data compression and uses ChaCha20 and BLAKE3 for encryption and authentication. Specifically, BPE tokenizes medical text, while Avro serializes JSON (JavaScript Object Notation) objects, contributing to data encryption. A proprietary tokenizer was trained, and compression efficiency was evaluated using a "Performance Benchmark Dataset." Comparative analyses were conducted to assess the compression efficiency of JSON serialization methods (Avro and ASN.1 [Abstract Syntax Notation One]), and tokenization algorithms (BPE and unigram). The dataset consisted of JSON files from 300 patients, averaging 240.1 fields per file (range 89-623) and 7095 bytes in size (range 2748-17,425 bytes). Using the BPE + Avro + Gzip algorithm, the average file size was reduced to 1048 bytes, achieving a compression ratio of 6.67. This was 1.82 times more efficient than traditional Gzip compression (average file size: 1907 bytes; compression ratio: 3.66; P<.001). For Chinese medical text tokenization, BPE outperformed unigram with a compression ratio of 4.68 versus 4.55 (P<.001). Avro and ASN.1 demonstrated comparable compression ratios of 2.57 and 2.59, respectively, when used alone (P=.30). However, Avro combined with BPE and Gzip significantly outperformed ASN.1, achieving compression ratios of 6.67 versus 5.21 (P<.001). Additionally, 84.7% (254/300) of patients needed to scan only 1 QR code, requiring an average of 3.1 seconds. The QR code-based security transmission algorithm using Avro and BPE efficiently compresses and transmits data in an encrypted manner and authenticates the identity of the scanning users, ensuring the privacy and security of medical data. Delivered as a software development kit, the algorithm offers straightforward implementation and usability, supporting its broad adoption across various applications.

Lung cancer continues to be one of the most widespread and deadly cancer diagnoses that affects humans worldwide. Early detection of lung cancer leads to decreased mortality rates; however, several challenges hinder the development and deployment of effective predictive models. These challenges consist of mainly the problem of high computational power to evaluate large-scale medical data, privacy and security of medical data, limited data sharing between medical organizations and interpretability to handle the black box problem that AI-based models face. Such limitations have posed severe difficulties to the utilization of conventional approaches in the prediction of lung cancer, thus limiting them most importantly for general use, especially in clinical practice settings in real time. To address these challenges, this research introduced a novel lung cancer prediction model that utilizes an integrated framework combining MapReduce, Private Blockchain, Federated Learning (FL), and Explainable Artificial Intelligence (XAI). It improves lung cancer detection using MapReduce to handle large lung cancer datasets, supporting rapid and scalable learning. Private Blockchain is used for the secure, tamper-proof, and immutable processing of patient information, whereas FL allows healthcare sectors to train models together, without compromising patients’ privacy. Moreover, it also employed XAI to improve the model’s interpretability so clinicians can understand and rely on AI predictions. Together, these methods improve AI’s efficiency and trustworthiness in medical applications. This proposed model provides better and more secure lung cancer predictions, ensuring interpretability and collaboration. With an exceptional accuracy of 98.21% and a miss rate of just 1.79%, it outperforms previously published approaches, establishing a new benchmark for privacy-preserving, explainable, and scalable AI models in healthcare.

The computer-aided diagnosis helps medical professionals detect and classify lung diseases from chest X-rays by leveraging medical image processing and central server-based machine learning models. These technologies provide real-time assistance to analyze the input and help efficiently detect the abnormalities at the earliest. However, traditional learning models are not suitable for live scenarios that require privacy, data diversity, and decentralized processing. The Federated learning-based model facilitates the protection of medical data privacy while processing a large volume of medical images, aiming to improve the overall efficiency of the model. This paper proposes a Federated Learning based Ensemble Model (FLEM) framework for an efficient diagnosis of lung diseases. The FLEM utilizes explainable AI techniques, including SHAP, Grad-CAM, and Differential Privacy, to provide transparency and interpretability of predictions while maintaining the privacy and security of medical data. We applied InceptionV3, Conv2D, VGG16, and ResNet-50 models on the COVID-19, TB, and pneumonia datasets and analysed the performance of the models in FLEM and Central Server-based Learning Model (CSLM). The performance analysis shows that the FLEM model outperformed the traditional CSLM model in terms of accuracy, training time, and bandwidth consumption. CSLM witnesses a quicker convergence time than FLEM. Although the CSLM model converged after a considerable number of epochs, it resulted in a 5, 8, 9, and 10% accuracy reduction compared to the FLEM-based training of InceptionV3, Conv2D, VGG16, and ResNet50 that achieved accuracies of 91.8, 88, 92.5, and 95.5%, respectively.

The perspectives of families, and especially parents of children with special health care needs, need to be better understood by the government agencies, health care providers, associations, and information systems vendors that are integrating child health information systems. To date, research on children with special health care needs has not included the perspectives of parents on integrated child health information systems. Interviews were conducted with a limited number of parents of children with special health care needs and a young adult with special health care needs about their perspectives on integrated health information systems needs. The interviews revealed common themes: (1) parents have experienced the benefits of information technology for health care purposes, (2) parents believe integrated health information systems could help to coordinate their children's health care, (3) parents believe information technology can help improve accuracy and timeliness of information, (4) parents of children with special health care needs believe their children's health information should be available to those who need it, but safeguards must be in place, (5) parents believe health information systems can improve health care, but it is not the highest priority health care issue for them, and (6) parents believe that their involvement in issues related to children with special health care needs, including information technology, is critical. Parents of children with special health care needs hold strong opinions about their children's health care, including health information systems. Parents need to be central to discussions about development of integrated child health information systems if we are to develop information systems that serve the needs of children with special health care needs and their families.

The rapid development of the Internet of Things technology has led to a boom in the adoption of intelligent healthcare management systems in the healthcare industry. However, it has also highlighted key issues such as security, privacy, and efficient query of medical data. Traditional methods for querying medical data suffer from severe data leakage risks, low query performance, and excessive storage space. This article proposes a comprehensive Secure ENcrypted Search for Health Scheme (SENSH) solution based on consortium blockchain and searchable encryption to address these challenges. SENSH enables efficient authorization management through Bloom filters, ensuring fast querying of large datasets by authorized users while saving storage space. It uses off-chain Advanced Encryption Standard (AES) and on-chain storage management for data protection, significantly reducing the likelihood of data exposure. The system is also enhanced with event triggering and logging mechanisms to support real-time monitoring and data tracing to meet audit compliance requirements. It provides version control and timestamping to accommodate dynamic data updates, employs an obfuscationfactor to prevent tag-based original data content leakage, and supports dynamic updating of tags to accommodate different access requirements. Experimental results show that SENSH excels in authorization management, privacy protection, defense against tampering, and anti-replay and Distributed Denial of Service (DDoS). Compared with existing schemes, SENSH has significant advantages in terms of gas consumption, computation cost, and execution time. It is particularly suited for the protection and efficient query of medical and health data.

In the swiftly evolving realm of healthcare technology, safeguarding the privacy and security of delicate medical data is of utmost importance. Conventional methods often prove inadequate in preserving patients’ confidentiality, compelling the need for innovative solutions. This research introduces a pioneering methodology called Selective Data Access through Privacy-Enhanced Blockchain. This approach integrates blockchain technology with advanced data analytics techniques, revolutionizing medical data management. By harnessing blockchain, the system restricts access to essential data, enhances privacy, and expedites predictive modeling. The attained outcomes are substantial: a potential 85% enhancement in data privacy, ensuring limited access to sensitive medical information, coupled with an estimated 80% reduction in processing time and resources for streamlined predictive modeling. Moreover, there is a prospective 70% cost saving in data storage and management. These results collectively underscore the transformative potential of the Selective Data Access through Privacy-Enhanced Blockchain methodology, promising optimized security, efficiency, and cost-effectiveness in healthcare data management.

Objectives: This study assessed the demographic and institutional factors associated with the utilization of Health Informatics Systems (HIS) in the Sagnarigu Municipality. Methods: Two hundred and forty-nine (249) healthcare providers were randomly selected from 25 of 63 health facilities in the Sagnarigu Municipality. These healthcare providers included both clinical and preventive staffs. Frequency and Chi-Square analyses were run on the data to establish associations. Results: The results showed that approximately 59% (n = 148) of healthcare providers indicated low utilization of HIS. Healthcare providers, qualification, the sub-musicality for which they work for and facility work experience were the demographic factors associated with the utilization of HI as they all had p-values&lt;0.05. On the other hand, the institutional factors that were associated with HIS utilization among the healthcare providers were adequate HIS training, user friendly(p=0.037), monitoring and evaluation (p &lt; 0.001), leadership support (p &lt; 0.001), regular HIS updates (p &lt; 0.001), adequate resources (p &lt; 0.00), policy for data security (p &lt; 0.001) as well as effective communication(p=0.033) Conclusion: Utilization of Health Informatics is low among the healthcare providers in the Sagnarigu Municipality. The Sagnarigu Health needs to provide in service training to healthcare providers of the usage of HIS and supply the needed resources to encourage and support the health facilities to improve healthcare delivery in the Sagnarigu Municipality. To enhance utilization of HIS, it will be essential that government supports health facilities who have challenges using HIS.

Investigating the literature reveals the fact that the key management protocols play a vital role in protecting the security and privacy of medical data in telecare medical information systems. Recently, Tseng et al . have proposed an interesting elliptic curve cryptosystem (ECC) based self-certified key management scheme that can yield secure channel for the communications of secure sensors (cluster members) and access point (cluster head). After careful consideration, we found that their scheme suffers from the cluster head impersonation, replay, and key replicating attacks. In addition, in the secure session phase, there are some errata and a point multiplication that is undefined in the ECC and hence is mathematically incorrect. Finally, yet importantly, their presented scheme has been adopted for a similar application by other authors with the same problem. Therefore, in this paper, we first try to elaborate the existing errata and security threats. Second, we propose a modified version, which is free from the challenges of their scheme. Eventually, we propose a novel anonymous ECC-based self-certified two-factor key management scheme that not only provides the desired security features, but also has much better efficiency than several recently-published schemes, such as the presented one by Tseng et al . Our formal security verification and proof besides the efficiency analysis support our claim.

Introduction: The implementation of digital solutions in primary health care has brought significant changes and improvements to traditional systems. However, the integration between traditional medical cultures and digital solutions remains a hurdle to be overcome. Despite this, the implementation of these solutions brings advantages such as improved access to care, increased patient engagement, enhanced efficiency, and better health outcomes. However, there are challenges to be overcome, such as resistance to change, concerns about privacy and data security, as well as infrastructure and internet access issues. Through collaboration between healthcare providers, policymakers, and technology developers, it is possible to successfully overcome these challenges and integrate digital solutions into primary health care. Methodology: An integrative literature review was conducted to analyze case studies and relevant research on the implementation of digital solutions in primary health care. Articles published between 2010 and 2023 were selected from databases such as PubMed, Scielo, and Medline, addressing the benefits, challenges, and outcomes related to digital solutions. Data analysis was performed qualitatively and quantitatively, grouping relevant information and highlighting improvements in primary health care. Objective: This scientific article aims to explore the impact of digital solutions in primary health care, comparing and contrasting traditional systems with the advantages and challenges of implementing digital solutions. Discussion: The implementation of digital solutions in primary health care has brought significant advantages, such as improved access to care, increased efficiency, and better health outcomes. However, it also presents challenges, including resistance to change, concerns about data privacy, and the need to assess cost-effectiveness. Case studies highlight both successful and unsuccessful implementations, providing valuable insights into the factors that contribute to success or failure. To harness the full potential of digital solutions in primary health care, it is crucial to consider the impact on the doctor-patient relationship, ensure effective data management and analysis, and integrate digital solutions into existing healthcare systems. Additionally, ethical considerations, adequate training for healthcare professionals, and patient acceptance play important roles in the successful implementation of these solutions. Conclusion: The implementation of digital solutions in primary health care brings various advantages, such as improved access, increased efficiency, and better health outcomes. However, it is essential to address ethical issues, such as data privacy and security, and ensure that these solutions are accepted, effective, and integrated into existing healthcare systems. Training and education for healthcare professionals play a crucial role in the successful adoption of these solutions, as well as patient acceptance and adoption. Looking to the future, advances in digital technology have great potential to transform primary health care, provided the necessary skills are considered and there is collaboration among stakeholders. By addressing these considerations, primary health care can make the most of the benefits of digital solutions, improving healthcare delivery for individuals.

The integration of blockchain technology in biomedical diagnostics offers a promising solution to the challenges of data security and privacy in infectious disease surveillance. As the digitalization of healthcare systems accelerates, the need to protect sensitive health information becomes increasingly critical. Blockchain, with its decentralized and immutable nature, provides a robust framework for ensuring the integrity and confidentiality of biomedical data. This abstract explores how blockchain technology can be leveraged to enhance data security and privacy in the context of infectious disease surveillance, where rapid and accurate data sharing is essential for effective public health responses. Infectious disease surveillance relies on the collection, analysis, and dissemination of large volumes of data, often shared across multiple institutions and geographical regions. Traditional systems for managing this data are vulnerable to breaches, unauthorized access, and data tampering, which can compromise public health efforts and patient privacy. Blockchain technology addresses these vulnerabilities by enabling secure, transparent, and tamper-proof data exchanges. Each transaction or data entry is recorded in a distributed ledger, accessible only to authorized participants, thus ensuring that the data remains secure and unaltered. Moreover, blockchain’s inherent transparency allows for real-time monitoring and auditing of data flows, which is crucial in the timely detection and response to infectious disease outbreaks. The use of smart contracts within blockchain networks further enhances the automation and efficiency of data management, ensuring that data is only accessed and shared according to predefined rules and conditions. This not only safeguards patient privacy but also builds trust among stakeholders, including patients, healthcare providers, and public health authorities. In conclusion, the integration of blockchain technology in biomedical diagnostics presents a transformative approach to addressing the critical issues of data security and privacy in infectious disease surveillance. By leveraging blockchain's unique features, healthcare systems can ensure that sensitive diagnostic data is protected, thus supporting more effective and secure public health interventions in the fight against infectious diseases. Keywords: Blockchain, Biomedical Diagnostics, Data Security, Privacy, Infectious Disease Surveillance.

The Internet of Things (IoT) has shown rapid growth in recent years. However, it presents challenges related to the lack of standardization of communication produced by different types of devices. Another problem area is the security and privacy of data generated by IoT devices. Thus, with the focus on grouping, analyzing, and classifying existing data security and privacy methods in IoT, based on data anonymization, we have conducted a Systematic Literature Review (SLR). We have therefore reviewed the history of works developing solutions for security and privacy in the IoT, particularly data anonymization and the leading technologies used by researchers in their work. We also discussed the challenges and future directions for research. The objective of the work is to give order to the main approaches that promise to provide or facilitate data privacy using anonymization in the IoT area. The study’s results can help us understand the best anonymization techniques to provide data security and privacy in IoT environments. In addition, the findings can also help us understand the limitations of existing approaches and identify areas for improvement. The results found in most of the studies analyzed indicate a lack of consensus in the following areas: (i) with regard to a solution with a standardized methodology to be applied in all scenarios that encompass IoT; (ii) the use of different techniques to anonymize the data; and (iii), the resolution of privacy issues. On the other hand, results made available by the k-anonymity technique proved efficient in combination with other techniques. In this context, data privacy presents one of the main challenges for broadening secure domains in applying privacy with anonymity.