Sensitive Patient Information Research Articles

Learning and diSentangling patient static information from time-series Electronic hEalth Records (STEER).

Recent work in machine learning for healthcare has raised concerns about patient privacy and algorithmic fairness. Previous work has shown that self-reported race can be predicted from medical data that does not explicitly contain racial information. However, the extent of data identification is unknown, and we lack ways to develop models whose outcomes are minimally affected by such information. Here we systematically investigated the ability of time-series electronic health record data to predict patient static information. We found that not only the raw time-series data, but also learned representations from machine learning models, can be trained to predict a variety of static information with area under the receiver operating characteristic curve as high as 0.851 for biological sex, 0.869 for binarized age and 0.810 for self-reported race. Such high predictive performance can be extended to various comorbidity factors and exists even when the model was trained for different tasks, using different cohorts, using different model architectures and databases. Given the privacy and fairness concerns these findings pose, we develop a variational autoencoder-based approach that learns a structured latent space to disentangle patient-sensitive attributes from time-series data. Our work thoroughly investigates the ability of machine learning models to encode patient static information from time-series electronic health records and introduces a general approach to protect patient-sensitive information for downstream tasks.

Fed-CL- an atrial fibrillation prediction system using ECG signals employing federated learning mechanism

Deep learning has shown great promise in predicting Atrial Fibrillation using ECG signals and other vital signs. However, a major hurdle lies in the privacy concerns surrounding these datasets, which often contain sensitive patient information. Balancing accurate AFib prediction with robust user privacy remains a critical challenge to address. We suggest Federated Learning , a privacy-preserving machine learning technique, to address this privacy barrier. Our approach makes use of FL by presenting Fed-CL, a advanced method that combines Long Short-Term Memory networks and Convolutional Neural Networks to accurately predict AFib. In addition, the article explores the importance of analysing mean heart rate variability to differentiate between healthy and abnormal heart rhythms. This combined approach within the proposed system aims to equip healthcare professionals with timely alerts and valuable insights. Ultimately, the goal is to facilitate early detection of AFib risk and enable preventive care for susceptible individuals.

Blockchain and FEF-Based Lightweight Anonymous Authentication Protocol for Wireless Medical Sensor Networks

Authentication ensures the privacy of patients by enabling access control within wireless medical sensor networks. However, many schemes do not consider the resource-constrained environments, making those protocols unusable. Meanwhile, sensitive patient information continues to be stored and accessed in a central location, increasing the risk of “single points of failure.” To solve this problem, the researchers designed a lightweight anonymous authentication protocol based on blockchain technology and fuzzy extraction. First, they created a multiround session key negotiation mechanism. Then, they utilized the fuzzy extraction function to extract and recover multimodal biometric features. Decentralization was achieved through blockchain and smart contracts. Simultaneously, the researchers also provided a formal security proof by Burrows-Abadi-Needham logic. Finally, experiments using the Java Pairing-Based Cryptography Library show that this scheme outperforms the comparison schemes in terms of computational overhead and communication overhead.

Visually security privacy medical data protection scheme using compressive sensing and improved duffing chaotic system

Health and medical data frequently contain sensitive patient information that must be protected. Existing visual security schemes for medical images exhibit limitations in the imperceptibility of cipher images and the performance of image reconstruction. This paper introduces and evaluates a novel approach called Visual Meaningful Image Encryption (VMIE) for securing medical images. The proposed VMIE scheme employs a chaotic system based on the Duffing equation for initial encryption. Medical images are processed and encrypted in a sparse domain. A Bidirectional Chaotic Magic Transformation (BCMT) algorithm is then applied to scramble the sparse medical images. The scrambled data undergoes compression and diffusion. An adaptive embedding strategy employing the Discrete Cosine Stockwell Transform (DCST) integrates confidential data into the host image. The performance of the proposed chaotic system is validated through theoretical analysis and numerical simulation. Simulation results, along with comparisons to existing schemes, demonstrate the efficacy of the VMIE method in enhancing visual security and its suitability for natural images. The VMIE approach presented in this paper offers a promising solution for securing medical images, effectively addressing the limitations of current visual security schemes.

Enhancing the Security of Information Systems Using Iot Technology

A medical patient information system is one of the systems that are currently in widespread use, and one of these is the psychiatric patient information system, which is used in clinics that treat patients who suffer from mental illnesses. It entails keeping track of the patients who visit these clinics for treatment and ensuring that they receive the necessary medications at the appropriate dosages. Patient information such as text, images, or others must be retained in this sort of system, and the reports that the system generates regularly for pharmaceuticals that are dispensed in various clinics separate this type of system from others. This study presents an innovative security strategy for accessing a managed care information system, emphasizing the use of IoT technology to enhance password security and protect sensitive patient information. Utilizing the Internet of Things (IoT) technology has enhanced the efficiency of creating secure passwords. The results of the tests showed that the goal of protecting patient information from people who didn't want to see it was very secure. This was shown in the literature.

Fortifying Patient Data Security in the Digital Era: A Two-Layer Approach with Data Hiding and Electrocardiogram

In an era dominated by digital technology, the imperative of securing patient data cannot be overstated. The deployment of advanced protective measures, including encryption, firewalls, and robust authentication protocols, is an absolute necessity when it comes to preserving the confidentiality and integrity of sensitive patient information. Furthermore, the establishment of stringent access controls serves as a fundamental safeguard, ensuring that only authorized personnel are granted access to this invaluable data. An innovative development in the realm of patient data protection is the utilization of ElectroCardioGram (ECG) as a unique identifier for individuals. In the context of this study, ECG data is ingeniously embedded within cover images using a technique known as Reversible Data Hiding (RDH). RDH offers a distinctive advantage by ensuring that the original image can be fully restored without loss of data after extraction. This achievement is made possible through the application of inventive pixel interpolation and histogram shifting algorithms. Crucially, the study's simulations, conducted across a diverse array of images, underscore the enhanced embedding capacity of the RDH technique while maintaining a commendable balance in terms of the Peak Signal to Noise Ratio (PSNR) and boundary map. This empirical evidence corroborates the efficacy of the approach and its potential to provide an advanced level of security for patient data in the digital landscape.

Tamper Detection and Self-recovery in a Visual Secret Sharing based Security Mechanism for Medical Records.

Medical records contain highly sensitive patient information. These medical records are significant for better research, diagnosis, and treatment. However, ensuring secure medical records storage is paramount to protect patient confidentiality, integrity, and privacy. Conventional methods involve encrypting and storing medical records in third-party clouds. Such storage enables convenient access and remote consultation. This cloud storage poses single-point attack risks and may lead to erroneous diagnoses and treatment. To address this, a novel (n,n)VSS scheme is proposed with data embedding, permutation ordered binary number system, tamper detection, and self-recovery mechanism. This approach enables the reconstruction of medical records even in the case of tampering. The tamper detection algorithm ensures data integrity. Simulation results demonstrate the superiority of proposed method in terms of security and reconstruction quality. Here, security analysis is done by considering attacks such as brute force, differential, and tampering attacks. Similarly, the reconstruction quality is evaluated using various human visual system parameters. The results show that the proposed technique provides a lower bit error rate ( ≈ 0), high average peak signal-to-noise ratio ( ≈ 35 dB), high structured similarity ( ≈ 1), high text embedding rate ( ≈ 0.7 BPP), and lossless reconstruction in the case of attacks.

Improved security of medical images using DWT–SVD watermarking mechanisms based on firefly Photinus search algorithm

Security is the primary concern in the transmission of medical images, as it involves sensitive patient information. This study introduces an optimized watermarking approach, constructed using discrete wavelet transform and singular value decomposition. The Low- level frequency bands (LL3) sub-band singular values of the host image were embedded with the singular values of a binary watermark using multiple scaling factors. These MSFs were optimized using a recently proposed firefly Photinus algorithm to balance robustness and imperceptibility. The proposed method was applied to various images, including computed tomography images, where the visual quality of the signed and attacked images was evaluated by peak signal-to-noise ratio (PSNR) and normalized cross-correlation. The performance of the proposed algorithm demonstrates significant improvements in the embedding and extraction processes, showing an enhancement in the balance between robustness and imperceptibility, with a PSNR above 79.28 dB, compared to other related works.

BIG DATA PRIVACY IN BIOMEDICAL RESERACH

Abstract: With the exponential growth of biomedical data, the potential for transformative discoveries in healthcare is immense. However, the utilization of such vast datasets raises significant concerns regarding data privacy and security. This project proposes a Java-based solution aimed at preserving privacy while enabling robust analysis of big biomedical datasets. The primary objective of this project is to develop a framework that facilitates secure and privacy-preserving data analysis in biomedical research. The framework employs advanced cryptographic techniques and secure multi-party computation protocols to ensure that sensitive patient information remains confidential throughout the analysis process To demonstrate the efficacy of the proposed solution, a prototype system will be developed and evaluated using real-world biomedical datasets. Performance metrics such as computational overhead, data transmission latency, and scalability will be assessed to validate the feasibility and effectiveness of the framework. In conclusion, this project aims to address the pressing need for privacy-preserving big data analysis in biomedical research, thereby facilitating responsible data sharing and advancing scientific discovery while safeguarding individual privacy rights. Keywords– Big data privacy, biomedical search, java project, privacy preserving techniques,HIPAA, GDPR.

A secured and energy-efficient system for patient e-healthcare monitoring using the Internet of Medical Things (IoMT)

Introduction: The Internet of Things (IoT) is gaining popularity in several industries owing to the autonomous and low-cost functioning of its sensors. In medical and healthcare usage, IoT gadgets provide an environment to detect patients' medical problems, such as blood volume, oxygen concentration, pulse, temperatures, etc. and take emergency action as necessary. The problem of imbalanced energy usage across biosensor nodes slows down the transmission of patient data to distant centres and has a detrimental effect on the health industry. In addition, the patient's sensitive information is sent through the insecure Internet and is exposed to potential threats. For clinical uses, information privacy and stability against hostile traffic constitute a further research challenge. Methods: This article proposes a Secured and Energy-Efficient System (SEES-IoMT) e-healthcare utilizing the Internet of Medical Things (IoMT) monitoring, the main goal of which is to reduce the connectivity cost and energy usage between sensing devices while feasibly forwarding the medical data. SEES-IoMT also guarantees the clinical data of the patients against unverified and malevolent nodes to enhance the privacy and security of the system.Result and Discussion: In consideration of the memory and power limitations of healthcare IoT gadgets, this approach is designed to be very lightweight. A thorough examination of this system's safety is performed to demonstrate its reliability.Conclusion: In terms of computing speed and security, the research compares SEES-IoMT to relevant methods in the IoT medical environment to demonstrate its applicability and resilience.

SPEI-FL: Serverless Privacy Edge Intelligence-Enabled Federated Learning in Smart Healthcare Systems

Smart healthcare systems promise significant benefits for fast and accurate medical decisions. However, working with personal health data presents new privacy issues and constraints that must be solved from a cybersecurity perspective. Edge intelligence-enabled federated learning is a new scheme that utilises decentralised computing that allows data analytics to be carried out at the edge of a network, enhancing data privacy. However, this scheme suffers from privacy attacks, including inference, free-riding, and man-in-the-middle attacks, especially with serverless computing for allocating resources to user needs. Edge intelligence-enabled federated learning requires client data insertion and deletion to authenticate genuine clients and a serverless computing capability to ensure the security of collaborative machine learning models. This work introduces a serverless privacy edge intelligence-based federated learning (SPEI-FL) framework to address these issues. SPEI-FL includes a federated edge aggregator and authentication method to improve the data privacy of federated learning and allow client adaptation and removal without impacting the overall learning processes. It also can classify intruders through serverless computing processes. The proposed framework was evaluated with the unstructured COVID-19 medical chest x-rays and MNIST digit datasets, and the structured BoT-IoT dataset. The performance of the framework is comparable with existing authentication methods and reported a higher accuracy than comparable methods (approximately 90% as compared with the 81% reported by peer methods). The proposed authentication method prevents the exposure of sensitive patient information during medical device authentication and would become the cornerstone of the next generation of medical security with serverless computing.

Advancing Healthcare Infrastructure: The Features of Iran’s Sina Electronic Health Record System

Background and aims: The Sina Electronic Health Record (SinaEHR) system was developed to support primary care delivery across Iran by enabling chronic disease monitoring, infectious disease surveillance, health policy decision-making, and quality improvement initiatives. Methods: The SinaEHR system was implemented by Mashhad University of Medical Sciences which contains health records of over 5 million people across various regions of Iran. It utilizes the International Classification of Diseases (ICD) version 10, integrating laboratory results through a laboratory information system to facilitate real-time clinical decision-making. Results: Widespread implementation of the SinaEHR system has established an invaluable healthcare infrastructure resource for the improvement of primary care services across Iran. Strict security and confidentiality measures are enforced to protect sensitive patient information. Conclusion: The SinaEHR system significantly advances Iran’s healthcare infrastructure by providing a comprehensive electronic health record platform to improve healthcare delivery and promote evidence-based decision-making.

Regulating Malpractice in Telemedicine and Digital Health

Abstract This paper employs the IRAC method to examine the legal and regulatory landscape governing malpractice in telemedicine and digital health in Ghana, focusing on patient privacy breaches, negligence by healthcare providers, and leakage of sensitive patient information. Drawing upon relevant Ghanaian laws and regulations, as well as insights from relevant research papers, the analysis highlights the need for updates to existing legal frameworks, development of targeted AI governance mechanisms, and promotion of transparency and accountability in the use of digital health technologies. The paper provides practical recommendations for policymakers, healthcare stakeholders, and legal professionals, emphasizing the importance of public education and provider training to foster a culture of responsible innovation and maintain public trust in the rapidly evolving healthcare system. These findings contribute to the development of evidence-based policies and practices that prioritize patient safety, rights, and trust in the digital health era.

Machine learning cryptography methods for IoT in healthcare

BackgroundThe increased application of Internet of Things (IoT) in healthcare, has fueled concerns regarding the security and privacy of patient data. Lightweight Cryptography (LWC) algorithms can be seen as a potential solution to address this concern. Due to the high variation of LWC, the primary objective of this study was to identify a suitable yet effective algorithm for securing sensitive patient information on IoT devices.MethodsThis study evaluates the performance of eight LWC algorithms—AES, PRESENT, MSEA, LEA, XTEA, SIMON, PRINCE, and RECTANGLE—using machine learning models. Experiments were conducted on a Raspberry Pi 3 microcontroller using 16 KB to 2048 KB files. Machine learning models were trained and tested for each LWC algorithm and their performance was evaluated based using precision, recall, F1-score, and accuracy metrics.ResultsThe study analyzed the encryption/decryption execution time, energy consumption, memory usage, and throughput of eight LWC algorithms. The RECTANGLE algorithm was identified as the most suitable and efficient LWC algorithm for IoT in healthcare due to its speed, efficiency, simplicity, and flexibility.ConclusionsThis research addresses security and privacy concerns in IoT healthcare and identifies key performance factors of LWC algorithms utilizing the SLR research methodology. Furthermore, the study provides insights into the optimal choice of LWC algorithm for enhancing privacy and security in IoT healthcare environments.

Smart Defense: How Self-Learning AI Can Shield Bangladeshi Medical Records

The digitalization of healthcare records in Bangladesh presents both opportunities and challenges, particularly concerning the security and protection of sensitive patient information. As electronic health records (EHRs) become increasingly prevalent, the threat of cyberattacks targeting medical data escalates, necessitating innovative solutions to fortify the country's healthcare cybersecurity infrastructure. This paper investigates the efficacy of self-learning artificial intelligence (AI) systems in safeguarding Bangladeshi medical records against cyber threats. The traditional methods of securing medical records, such as firewalls and antivirus software, are proving inadequate against the evolving tactics of cybercriminals. Bangladesh faces unique challenges in this regard, including limited resources, lack of cybersecurity awareness among healthcare professionals, technological fragmentation, and an increasingly sophisticated threat landscape. To address these challenges, there is a growing imperative to explore novel approaches that can adapt and evolve in real-time to counter emerging cyber threats. Self-learning AI systems represent a promising frontier in healthcare cybersecurity. By leveraging advanced machine learning algorithms, these systems can analyze vast amounts of data to detect patterns indicative of cyber threats. Unlike static security measures, self-learning AI continuously learns from new information and adjust their defense strategies, accordingly, enabling them to stay ahead of evolving threats. Key functionalities of self-learning AI include anomaly detection, threat prediction, and adaptive defense mechanisms, all of which are essential for safeguarding medical records in Bangladesh's healthcare landscape. The implications of integrating self-learning AI into Bangladesh's healthcare cybersecurity framework are significant. Not only can these technologies enhance the detection and prevention of cyber threats, but they can also alleviate resource constraints and technical challenges faced by healthcare organizations. However, successful implementation requires comprehensive training, adherence to data privacy regulations, and ongoing monitoring to ensure the effectiveness and reliability of AI-driven security measures. The protection of medical records is paramount as Bangladesh continues its digital transformation in healthcare. Self-learning AI offers a dynamic and proactive approach to cybersecurity, empowering healthcare organizations to mitigate risks and preserve patient privacy in an increasingly digitized landscape. Embracing these innovative technologies is crucial for building a resilient healthcare ecosystem that prioritizes data security and patient trust.

A Two-Level Security Layer for Medical Data

Implementing a two-level security approach comprising cryptography and steganography in Electronic Health Record (EHR) systems ensures comprehensive protection of sensitive patient information. Cryptography encryption methods alone may not provide adequate protection, as they can be susceptible to attacks and also, steganography, has the visual quality issues after embedding. The combination of RSA cryptography and DWT steganography will offers a comprehensive solution for securing medical data, ensuring confidentiality, integrity, and compliance with privacy regulations, while DWT steganography provides covert embedding within medical images, preserving diagnostic integrity and enhancing data protection. In this paper, a secure two-level layer of medical data security is proposed to encrypt secret information using Rivest-Shamir-Adleman (RSA) while the encrypted secret information is embedded using Discrete Wavelet Transform (DWT). The performance of the proposed method was evaluated using Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Normalized Cross-Correlation (NCC) metrics. The results obtained show that our method performs with a value of 0.105 for MSE, 49.713 for PSNR and 0.912 for NCC showing high visual quality and low imperceptibility.

Enhancing Healthcare Data Security Using Quantum Cryptography for Efficient and Robust Encryption

Healthcare requires data encryption to safeguard sensitive patient information from unauthorized access or breaches. Encryption ensures that data is encoded into a secure format, making it unreadable to anyone without the decryption key. This helps maintain patient privacy, protects against identity theft, and ensures compliance with data protection regulations like HIPAA (Health Insurance Portability and Accountability Act.). With encryption, healthcare organizations can mitigate the risk of data breaches and uphold trust with patients by ensuring the confidentiality and integrity of their medical records and personal information. Quantum cryptography emerges as a revolutionary solution, offering unparalleled encryption for healthcare data. This process initiates with the meticulous crafting of quantum states to encapsulate healthcare data securely, ensuring precise encoding. Subsequently, quantum entanglement is established between sender and receiver, creating a secure communication channel resistant to interception attempts. Quantum key distribution then generates a secure key leveraging entangled quantum states, becoming the cornerstone of encryption. Quantum encryption shields healthcare data in an impenetrable veil of secrecy, leveraging the complexities of quantum mechanics to thwart unauthorized decryption attempts. The decryption process, facilitated by the shared secure key, unveils the encrypted healthcare data exclusively to authorized parties, preserving confidentiality with unparalleled precision. The result reveals quantum cryptography's superiority in healthcare data encryption. At 150-bit key length, quantum cryptography takes 352,237 milliseconds vs. AES's 310,285 milliseconds. For a 14 KB input, quantum cryptography requires 7 milliseconds compared to AES's 12 milliseconds. These metrics highlight quantum cryptography's efficiency gains and transformative potential in healthcare data security, promising both enhanced security and efficiency benefits.

Enhancing Healthcare Data Security Using Quantum Cryptography for Efficient and Robust Encryption

Healthcare requires data encryption to safeguard sensitive patient information from unauthorized access or breaches. Encryption ensures that data is encoded into a secure format, making it unreadable to anyone without the decryption key. This helps maintain patient privacy, protects against identity theft, and ensures compliance with data protection regulations like HIPAA (Health Insurance Portability and Accountability Act.). With encryption, healthcare organizations can mitigate the risk of data breaches and uphold trust with patients by ensuring the confidentiality and integrity of their medical records and personal information. Quantum cryptography emerges as a revolutionary solution, offering unparalleled encryption for healthcare data. This process initiates with the meticulous crafting of quantum states to encapsulate healthcare data securely, ensuring precise encoding. Subsequently, quantum entanglement is established between sender and receiver, creating a secure communication channel resistant to interception attempts. Quantum key distribution then generates a secure key leveraging entangled quantum states, becoming the cornerstone of encryption. Quantum encryption shields healthcare data in an impenetrable veil of secrecy, leveraging the complexities of quantum mechanics to thwart unauthorized decryption attempts. The decryption process, facilitated by the shared secure key, unveils the encrypted healthcare data exclusively to authorized parties, preserving confidentiality with unparalleled precision. The result reveals quantum cryptography's superiority in healthcare data encryption. At 150-bit key length, quantum cryptography takes 352,237 milliseconds vs. AES's 310,285 milliseconds. For a 14 KB input, quantum cryptography requires 7 milliseconds compared to AES's 12 milliseconds. These metrics highlight quantum cryptography's efficiency gains and transformative potential in healthcare data security, promising both enhanced security and efficiency benefits.

EHealth Innovation for Chronic Obstructive Pulmonary Disease: A Context-Aware Comprehensive Framework

Chronic Obstructive Pulmonary Disease (COPD) poses a significant global healthcare challenge. It is a progressive lung disease that causes breathing difficulties and can significantly impact a person's quality of life. COPD is primarily caused by smoking, but other factors, such as air pollution and genetic predisposition, can also contribute to its development. This paper introduces a novel Context-Aware Framework for the Diagnosis and Personalized Management of COPD. We discuss the limitations of traditional COPD management, highlighting the importance of early detection and remote monitoring. Early detection and remote monitoring are crucial in managing COPD as they allow for timely interventions and better disease management. In this paper, we propose a framework based mostly on contextual data and other parameters of COPD as put forth by the World Health Organization (WHO) in the form of the International Classification of Functioning, Disability, and Health. Ontologies drive this architecture and incorporate dynamic contextual information from patient environments, user profiles, and sensor data. In addition to the various obvious data items like patient personal details (gender, contact, medical history) and COPD risks and symptoms, the COPD ontology also considers the details about the caregiver and healthcare professional. This is in addition to the contextual data processed separately using the Context Ontology. The ontology we constructed using Protégé serves as the framework for the structured representation and logical inference of contextual information. By harnessing dynamic contextual data, our ontology enables real-time decision-making tailored to individual patient requirements. It empowers healthcare professionals to make informed choices and deliver timely interventions, enhancing healthcare services by offering proactive care to detect early signs of health deterioration and suggest preventive measures. This approach improves patient experiences and optimizes resource allocation within the healthcare system. To uphold ethical standards and prioritize the needs of patients, we emphasize the significance of safeguarding data, obtaining informed permission, and recognizing data ownership. The ontology-based approach presented in this study offers a scalable and flexible framework that can be readily incorporated into existing healthcare systems, redefining the management of COPD in response to evolving demands. Security poses one of the biggest threats in context-based environments due to the different data formats acquired by the diverse sensors. Another essential consideration is confidentiality because the data in hand is sensitive patient information.

Improving Healthcare Data Security and Privacy With Blockchain technology using SHA-256 Algorithm

Block chain technology has the potential to completely transform healthcare management systems by enhancing data security, interoperability, and integrity because to its decentralized and unchangeable structure. Sensitive patient information is more difficult for bad actors to breach thanks to the SHA-256 algorithm, which improves data security. Interoperability between diverse healthcare systems is facilitated by smart contracts and standardized data formats. However, issues with user adoption, scalability, and regulatory compliance also face block chain implementation in the healthcare industry. This study offers important insights into the changing environment of healthcare data management by highlighting the possible advantages and challenges of integrating block chain technology in healthcare management systems.