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As network technology advances and more people use devices, data storage has become a significant challenge due to the explosive growth of information and the threat of data leaks. In traditional medical institutions, most medical data is stored centrally through cloud computing technology in the institution's data center. This centralized storage method has many security risks, and once the central server is attacked, it will lead to the loss of medical data, which will lead to the leakage of patients' private data. At the same time, electronic medical records are the most critical data in the current medical field. In the traditional centralized healthcare service system (HSS), there are data leakage problems and tampering with electronic medical records due to human factors. At the same time, each hospital is built independently, resulting in the current centralized healthcare service system having a data silo problem, making it difficult to share medical data between institutions securely. With the increase in the number of users in the system, the electronic medical record data in the system also increases gradually, resulting in the increasing overhead of decryption calculation. Therefore, this paper proposes a blockchain-based access control scheme with multiparty authorization to ensure the security of electronic medical records. The scheme uses an SM encryption algorithm to encrypt the medical data in the system. It adds the patient's signature to ensure the confidentiality and security of the data, and the encrypted electronic medical records (EMRs) are stored in the InterPlanetary File System (IPFS) to realize the distributed storage of EMR. In addition, role-based multiauthorization access control is implemented through smart contract-based to ensure the security of EMR. We have analyzed the security of this paper's solution and compared its performance with the existing schemes based on other cryptographic algorithms. The experimental results show that the proposed solution significantly improves the secure sharing of EMR and provides system performance. [ABSTRACT FROM AUTHOR]

Without proper security mechanisms, medical records stored electronically can be accessed more easily than physical files. Patient health information is scattered throughout the hospital environment, including laboratories, pharmacies, and daily medical status reports. The electronic format of medical reports ensures that all information is available in a single place. However, it is difficult to store and manage large amounts of data. Dedicated servers and a data center are needed to store and manage patient data. However, self-managed data centers are expensive for hospitals. Storing data in a cloud is a cheaper alternative. The advantage of storing data in a cloud is that it can be retrieved anywhere and anytime using any device connected to the Internet. Therefore, doctors can easily access the medical history of a patient and diagnose diseases according to the context. It also helps prescribe the correct medicine to a patient in an appropriate way. The systematic storage of medical records could help reduce medical errors in hospitals. The challenge is to store medical records on a third-party cloud server while addressing privacy and security concerns. These servers are often semi-trusted. Thus, sensitive medical information must be protected. Open access to records and modifications performed on the information in those records may even cause patient fatalities. Patient-centric health-record security is a major concern. End-to-end file encryption before outsourcing data to a third-party cloud server ensures security. This paper presents a method that is a combination of the advanced encryption standard and the elliptical curve Diffie-Hellman method designed to increase the efficiency of medical record security for users. Comparisons of existing and proposed techniques are presented at the end of the article, with a focus on the analyzing the security approaches between the elliptic curve and secret-sharing methods. This study aims to provide a high level of security for patient health records. [ABSTRACT FROM AUTHOR]

Cloud-based electronic medical record (EMR) management system is an emerging paradigm that enables EMR owners to store and share their EMRs with intended group of healthcare personnel. And, it allows EMR users to retrieve desired records from the cloud when require. However, authentic EMR sharing and searching with security has been a challenging problem. In this paper, for the first time, we propose a searchable attribute-based signcryption for EMR storage and retrieval in cloud computing environment, termed as MediCare, that supports simultaneously the functionalities: fine-grained access control over encrypted EMRs, EMR owner authenticity and anonymity, Boolean formula-based keyword search over encrypted EMRs, keyword privacy, constant decryption cost for EMR users, non-interactive search results verification, and provably secure. We employ attribute-based framework in designing MediCare. Only authorized EMR owners can anonymously upload EMRs to the cloud, and an EMR user can search over encrypted EMRs using Boolean formula keyword policy. MediCare enables an EMR user to verify the correctness of the search results efficiently without interacting with any authority. We define more general security definitions of MediCare and provide rigorous security analysis. We also conduct experiments to evaluate MediCare's performance. [ABSTRACT FROM AUTHOR]

On October 31, 2021, I learned the electronic health record in my independent, solo practice had been attacked by a Russian syndicate who was holding our data and our practice management system for "ransom." An encryption key could be given to our cloud provider once $5,100,000 was delivered in bitcoin to the hacking entity. After 3 long months of negotiations, with us going back to a completely paper-based system in the interim, our cloud provider paid the Russian syndicate and access was restored. There were many lessons to be learned from our experience. We were fortunate, and through the help of many of our business associates we were able to survive and live to see another day. [ABSTRACT FROM AUTHOR]

Over the past few years, increasing attention has been given to the health sector and the integration of new technologies into it. Cloud computing and storage clouds have become essentially state of the art solutions for other major areas and have started to rapidly make their presence powerful in the health sector as well. More and more companies are working toward a future that will allow healthcare professionals to engage more with such infrastructures, enabling them a vast number of possibilities. While this is a very important step, less attention has been given to the citizens. For this reason, in this paper, a citizen-centered storage cloud solution is proposed that will allow citizens to hold their health data in their own hands while also enabling the exchange of these data with healthcare professionals during emergency situations. Not only that, in order to reduce the health data transmission delay, a novel context-aware prefetch engine enriched with deep learning capabilities is proposed. The proposed prefetch scheme, along with the proposed storage cloud, is put under a two-fold evaluation in several deployment and usage scenarios in order to examine its performance with respect to the data transmission times, while also evaluating its outcomes compared to other state of the art solutions. The results show that the proposed solution shows significant improvement of the download speed when compared with the storage cloud, especially when large data are exchanged. In addition, the results of the proposed scheme evaluation depict that the proposed scheme improves the overall predictions, considering the coefficient of determination ( R 2 > 0.94) and the mean of errors (RMSE < 1), while also reducing the training data by 12%. [ABSTRACT FROM AUTHOR]

The application of medical cloud storage technology in healthcare and the sharing of electronic medical records (EMR) bring convenience for patients and medical institutes. However, two barriers limit further expansions of the above (i.e. key escrow and key abuse issues). In this paper, we construct a large universe data-sharing scheme based on attribute-based encryption. We design all attribute authorities simultaneously to participate in the key computation, and the user performs aggregation. By issuing anonymous credentials to the recipients, their identity information is protected. To achieve complete tracing of the traitor, we blend the two mechanisms, white-box traceability and black-box traceability, together. Detailed security proofs have been carried out for various types of possible attackers, and theoretical analyses have verified the security of the proposed scheme. We performed performance evaluations in conjunction with existing schemes, and numerical experience shows that the burden on the user side is also minimal. • No key escrow issues, resistant to collusion attacks among authorities. • No key abuse issues, multiple types of privilege leaks can be traced. • Large universe and anonymity, the number of attributes is unbounded. • Good trade-off between security and efficiency. [ABSTRACT FROM AUTHOR]

Due to the high amount of electronic health records, hospitals have prioritized data protection. Because it uses parallel computing and is distributed, the security of the cloud cannot be guaranteed. Because of the large number of e-health records, hospitals have made data security a major concern. The cloud's security cannot be guaranteed because it uses parallel processing and is distributed. The blockchain (BC) has been deployed in the cloud to preserve and secure medical data because it is particularly prone to security breaches and attacks such as forgery, manipulation, and privacy leaks. An overview of blockchain (BC) technology in cloud storage to improve healthcare system security can be obtained by reading this paper. First, we will look at the benefits and drawbacks of using a basic cloud storage system. After that, a brief overview of blockchain cloud storage technology will be offered. Many researches have focused on using blockchain technology in healthcare systems as a possible solution to the security concerns in healthcare, resulting in tighter and more advanced security requirements being provided. This survey could lead to a blockchain-based solution for the protection of cloud-outsourced healthcare data. Evaluation and comparison of the simulation tests of the offered blockchain technology-focused studies can demonstrate integrity verification with cloud storage and medical data, data interchange with reduced computational complexity, security, and privacy protection. Because of blockchain and IT, business warfare has emerged, and governments in the Middle East have embraced it. Thus, this research focused on the qualities that influence customers' interest in and approval of blockchain technology in cloud storage for healthcare system security and the aspects that increase people's knowledge of blockchain. One way to better understand how people feel about learning how to use blockchain technology in healthcare is through the United Theory of Acceptance and Use of Technology (UTAUT). A snowball sampling method was used to select respondents in an online poll to gather data about blockchain technology in Middle Eastern poor countries. A total of 443 randomly selected responses were tested using SPSS. Blockchain adoption has been shown to be influenced by anticipation, effort expectancy, social influence (SI), facilitation factors, personal innovativeness (PInn), and a perception of security risk (PSR). Blockchain adoption and acceptance were found to be influenced by anticipation, effort expectancy, social influence (SI), facilitating conditions, personal innovativeness (PInn), and perceived security risk (PSR) during the COVID-19 pandemic, as well as providing an overview of current trends in the field and issues pertaining to significance and compatibility. [ABSTRACT FROM AUTHOR]

In the rapidly evolving landscape of digital systems and modern technology, including computing and device peripherals, a paradigm shift is evident across various engineering fields. This transformation is characterized by the integration of cutting-edge concepts such as Artificial Intelligence (AI), Machine Learning, Deep Learning, Cloud Computing, and Smart Digital Systems. Notably, these advancements hold significant promise in Biomedical Engineering, where they play crucial roles in comprehending human conditions and anatomy. Given the paramount importance of health, especially amidst rising diseases and health-related challenges, there arises a pressing need for optimal solutions. This research focuses on the integration of Electronic Health Record (EHR) systems with AI to deliver efficient solutions. Specifically, the research targets issues related to prioritization and segment queue management, with the aim of enhancing overall proficiency and efficiency in healthcare operations. The research presents a prototype version developed, deployed, and experimentally evaluated to assess its performance in achieving the stated objectives. Through this investigation, the research seeks to contribute to the advancement of EHR systems and AI integration in healthcare, ultimately aiming to enhance patient care and healthcare delivery processes. [ABSTRACT FROM AUTHOR]

In the context of scaling a business-critical medical service that involves electronic medical record storage deployed in Kubernetes clusters, this research addresses the need to optimize the configuration parameters of horizontal pod autoscalers for maintaining the required performance and system load constraints. The maximum entropy principle was used for calculating a load profile to satisfy workload constraints. By observing the fluctuations in the existing workload and applying a kernel estimator to smooth its trends, we propose a methodology for calculating the threshold parameter of a maximum number of pods managed by individual autoscalers. The results obtained indicate significant computing resource savings compared to autoscalers operating without predefined constraints. The proposed optimization method enables significant savings in computational resource utilization during peak loads in systems managed by Kubernetes. For the investigated case study, applying the calculated vector of maximum pod count parameter values for individual autoscalers resulted in about a 15% reduction in the number of instantiated nodes. The findings of this study provide valuable insights for efficiently scaling services while meeting performance demands, thus minimizing resource consumption when deploying to computing clouds. The results enhance our comprehension of resource optimization strategies within cloud-based microservice architectures, transcending the confines of specific domains or geographical locations. [ABSTRACT FROM AUTHOR]

In recent decades, the cloud computing contributes a prominent role in health care sector as the patient health records are transferred and collected using cloud computing services. The doctors have switched to cloud computing as it provides multiple advantageous measures including wide storage space and easy availability without any limitations. This necessitates the medical field to be redesigned by cloud technology to preserve information about patient's critical diseases, electrocardiogram (ECG) reports, and payment details. The proposed work utilizes a hybrid cloud pattern to share Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) resources over the private and public cloud. The stored data are categorized as significant and non-significant by Artificial Neural Networks (ANN). The significant data undergoes encryption by Lagrange key management which automatically generates the key and stores it in the hidden layer. Upon receiving the request from a secondary user, the primary user verifies the authentication of the request and transmits the key via Gmail to the secondary user. Once the key matches the key in the hidden layer, the preserved information will be shared between the users. Due to the enhanced privacy preserving key generation, the proposed work prevents the tracking of keys by malicious users. The outcomes reveal that the introduced work provides improved success rate with reduced computational time. [ABSTRACT FROM AUTHOR]

Precision medicine provides a holistic perspective of an individual's health, including genetic, environmental, and lifestyle aspects to realize individualized therapy. The development of the internet of things (IoT) devices, the widespread emergence of electronic medical records (EMR), and the rapid progress of cloud computing and artificial intelligence provide an opportunity to collect healthcare big data throughout the lifespan and analyze the disease risk at all stages of life. Thus, the focus of precision medicine is shifting from treatment toward prediction and prevention, i.e., precision health. To this end, various types of data such as omics, imaging, EMR, continuous physiological monitoring, lifestyle, and environmental information, need to be collected, tracked, managed and shared. Thus, internet-of-medical things (IoMT) is crucial for assimilating the health systems, applications, services, and devices that can improve the speed and accuracy of diagnosis and treatments along with real-time monitoring and modification of patient behavior as well as health status. However, security has emerged as a growing concern owing to the proliferation of IoMT devices. The increasing interconnectivity of IoMT-enabled devices with health data reception, transmission, and processing significantly increases the number of potential vulnerabilities within a system. To address the security issues of precision health in IoMT systems, this study reviews the state-of-the-art techniques and schemes from the perspective of a hierarchical system architecture. We present an IoMT system model comprising three layers: the sensing layer, network layer, and cloud infrastructure layer. In particular, we discuss the vulnerabilities and threats to security in each layer and review the existing security techniques and schemes corresponding to the system components along with their functionalities. Owing to the unique nature of biometric features in medical and health services, we highlight the biometrics-based technologies applied in IoMT systems, which contribute toward a considerable difference between the security solutions of existing IoT systems. Furthermore, we summarize the challenges and future research directions of IoMT systems to ensure an improved and more secure future of precision health. [ABSTRACT FROM AUTHOR]

Recently, in healthcare organizations, real-time data have been collected from connected or implantable sensors, layered protocol stacks, lightweight communication frameworks, and end devices, named the Internet-of-Medical-Things (IoMT) ecosystems. IoMT is vital in driving healthcare analytics (HA) toward extracting meaningful data-driven insights. Recently, concerns have been raised over data sharing over IoMT, and stored electronic health records (EHRs) forms due to privacy regulations. Thus, with less data, the analytics model is deemed inaccurate. Thus, a transformative shift has started in HA from centralized learning paradigms towards distributed or edge-learning paradigms. In distributed learning, federated learning (FL) allows for training on local data without explicit data-sharing requirements. However, FL suffers from a high degree of statistical heterogeneity of learning models, level of data partitions, and fragmentation, which jeopardizes its accuracy during the learning and updating process. Recent surveys of FL in healthcare have yet to discuss the challenges of massive distributed datasets, sparsification, and scalability concerns. Because of this gap, the survey highlights the potential integration of FL in IoMT, the FL aggregation policies, reference architecture, and the use of distributed learning models to support FL in IoMT ecosystems. A case study of a trusted cross-cluster-based FL, named Cross-FL, is presented, highlighting the gradient aggregation policy over remotely connected and networked hospitals. Performance analysis is conducted regarding system latency, model accuracy, and the trust of consensus mechanism. The distributed FL outperforms the centralized FL approaches by a potential margin, which makes it viable for real-IoMT prototypes. As potential outcomes, the proposed survey addresses key solutions and the potential of FL in IoMT to support distributed networked healthcare organizations. [ABSTRACT FROM AUTHOR]

Background: Acute stroke care demands fast procedures performed through the collaboration of multiple professionals across multiple organizations. Cloud computing and the wide adoption of electronic medical records (EMRs) enable health care systems to improve data availability and facilitate sharing among professionals. However, designing a secure and privacy-preserving EMR cloud-based application is challenging because it must dynamically control the access to the patient’s EMR according to the needs for data during treatment. Objective: We developed a prototype of a secure EMR cloud-based application. The application explores the security features offered by the eHealth cloud-based framework created by the Advanced Secure Cloud Encrypted Platform for Internationally Orchestrated Solutions in Health Care Horizon 2020 project. This study aimed to collect impressions, challenges, and improvements for the prototype when applied to the use case of secure data sharing among acute care teams during emergency treatment in the Netherlands. Methods: We conducted 14 semistructured interviews with medical professionals with 4 prominent roles in acute care: emergency call centers, ambulance services, emergency hospitals, and general practitioner clinics. We used in-depth interviews to capture their perspectives about the application’s design and functions and its use in a simulated acute care event. We used thematic analysis of interview transcripts. Participants were recruited until the collected data reached thematic saturation. Results: The participants’ perceptions and feedback are presented as 5 themes identified from the interviews: current challenges (theme 1), quality of the shared EMR data (theme 2), integrity and auditability of the EMR data (theme 3), usefulness and functionality of the application (theme 4), and trust and acceptance of the technology (theme 5). The results reinforced the current challenges in patient data sharing during acute stroke care. Moreover, from the user point of view, we expressed the challenges of adopting the Advanced Secure Cloud Encrypted Platform for Internationally Orchestrated Solutions in Health Care Acute Stroke Care application in a real scenario and provided suggestions for improving the proposed technology’s acceptability. Conclusions: This study has endorsed a system that supports data sharing among acute care professionals with efficiency, but without compromising the security and privacy of the patient. This explorative study identified several significant barriers to and improvement opportunities for the future acceptance and adoption of the proposed system. Moreover, the study results highlight that the desired digital transformation should consider integrating the already existing systems instead of requesting migration to a new centralized system. [ABSTRACT FROM AUTHOR]

The blockchain is a peer-to-peer distributed ledger technology that works on the precept of "write-once-read-only." In a blockchain, pieces of information are arranged in the form of blocks, and these blocks are linked together using the hash value of previous blocks. The blocks in a blockchain mechanism are appended only, which means that once information is stored in a block and it cannot be changed; no one tampers the block's content. The traditional electronic medical records (EMRs) based system stores the patients' information in a local database or server, which provides centralization of information, and traditional EMRs are more centric on the health providers. So, security and sharing of patients' information are difficult tasks in the traditional EMR system. The blockchain mechanism has the potential to resolve these existing problems. Due to the appended-only-ledger principle and decentralization of blocks between the network participants, blockchain technology is suited to the EMR system. In this article, first, we discuss all the existing EMR systems and discuss their drawbacks. Keeping all the drawbacks in our mind, we propose a blockchain-based medical record system that utilizes clouding technology for storage purposes. Furthermore, we have designed a smart contract and consensus algorithm for our proposed EMR. Our system only uses a permissioned blockchain model so that only verified and authenticated users can generate their data and participate in the data-sharing system. [ABSTRACT FROM AUTHOR]

Traditional healthcare services have changed into modern ones in which doctors can diagnose patients from a distance. All stakeholders, including patients, ward boy, life insurance agents, physicians, and others, have easy access to patients' medical records due to cloud computing. The cloud's services are very cost-effective and scalable, and provide various mobile access options for a patient's electronic health records (EHRs). EHR privacy and security are critical concerns despite the many benefits of the cloud. Patient health information is extremely sensitive and important, and sending it over an unencrypted wireless media raises a number of security hazards. This study suggests an innovative and secure access system for cloud-based electronic healthcare services storing patient health records in a third-party cloud service provider. The research considers the remote healthcare requirements for maintaining patient information integrity, confidentiality, and security. There will be fewer attacks on e-healthcare records now that stakeholders will have a safe interface and data on the cloud will not be accessible to them. End-to-end encryption is ensured by using multiple keys generated by the key conclusion function (KCF), and access to cloud services is granted based on a person's identity and the relationship between the parties involved, which protects their personal information that is the methodology used in the proposed scheme. The proposed scheme is best suited for cloud-based e-healthcare services because of its simplicity and robustness. Using different Amazon EC2 hosting options, we examine how well our cloud-based web application service works when the number of requests linearly increases. The performance of our web application service that runs in the cloud is based on how many requests it can handle per second while keeping its response time constant. The proposed secure access scheme for cloud-based web applications was compared to the Ethereum blockchain platform, which uses internet of things (IoT) devices in terms of execution time, throughput, and latency. [ABSTRACT FROM AUTHOR]

Transferring medical imaging studies from one institution to another is a common occurrence in today's medical practice. For the past two decades, radiology departments have relied on physical media (compact discs and digital video disks) and human couriers to accomplish image transfer. This mode of transfer is slow, prone to failure, and reliant on outdated technology. To address these shortcomings, multiple image-sharing vendors have created electronic, cloud-based solutions. While these solutions solve multiple problems, a new problem has been introduced: it is difficult to send or receive images across image-sharing platforms. In this work, we describe how we have developed a solution to share images across multiple vendor platforms. [ABSTRACT FROM AUTHOR]

Cloud computing, financial incentive and patient-centered care are the game changers that deepen EHR diffusion beyond breadth. Based on the innovation diffusion theory (IDT), technology-organization-environment (TOE) framework and alignment literature, this study examines how these changes shape business requirement, service value and society need that drive different phases of EHR diffusion in terms of planning, adoption, usage and upgrade. A longitudinal analysis with the USA National Ambulatory Medical Care Survey (NAMCS) reveals the impacts of different drivers on EHR diffusion. In addition to quantitative results, interview observations corroborate the relationships among game changers, decision drivers and EHR diffusion. The findings provide healthcare providers, system vendors and policy-makers the insights on the best practices of promoting EHR diffusion for long-term success. [ABSTRACT FROM AUTHOR]

Central management of electronic medical systems faces a major challenge because it requires trust in a single entity that cannot effectively protect files from unauthorized access or attacks. This challenge makes it difficult to provide some services in central electronic medical systems, such as file search and verification, although they are needed. This gap motivated us to develop a system based on blockchain that has several characteristics: decentralization, security, anonymity, immutability, and tamper-proof. The proposed system provides several services: storage, verification, and search. The system consists of a smart contract that connects to a decentralized user application through which users can transact with the system. In addition, the system uses an interplanetary file system (IPFS) and cloud computing to store patients' data and files. Experimental results and system security analysis show that the system performs search and verification tasks securely and quickly through the network. [ABSTRACT FROM AUTHOR]

Cloud environment greatly necessitates two key factors namely integrity and memory consumption. In the proposed work, an efficient integrity check system (EICS) is presented for electronic health record (EHR) classification. The existing system does not concentrate on storage concerns such as storing and retrieving files in cloud and memory storage overheads. De-duplication is one of the solution, however original information loss might take place. This is mitigated by the suggested research work namely Integrity and Memory Consumption aware De-duplication Method (IMCDM), where health care files are stored in secured and reliable manner. File Indexed table are created for all the files for enhancing de-duplication performance before uploading it into server. Duplication existence can be obtained from the indexing table which comprises of file features and hash values. Support vector machine (SVM) classifier is used in indexing table construction for file feature learning. Labels allotted through SVM classifier is considered as index values. Two level encryption is used followed by indexing construction, and stored in cloud severs. For avoiding redundant data, a decrypted hash index comparison is performed with previously stored contents. Various security key based on individual user's generation is carried for ensuring security and XOR operation is performed with received encrypted file. The evaluation is performed using the Java simulation tool, which aids in validating the proposed methodology against existing research. [ABSTRACT FROM AUTHOR]

Over the past few years, increasing attention has been given to the health sector and the integration of new technologies into it. Cloud computing and storage clouds have become essentially state of the art solutions for other major areas and have started to rapidly make their presence powerful in the health sector as well. More and more companies are working toward a future that will allow healthcare professionals to engage more with such infrastructures, enabling them a vast number of possibilities. While this is a very important step, less attention has been given to the citizens. For this reason, in this paper, a citizen-centered storage cloud solution is proposed that will allow citizens to hold their health data in their own hands while also enabling the exchange of these data with healthcare professionals during emergency situations. Not only that, in order to reduce the health data transmission delay, a novel context-aware prefetch engine enriched with deep learning capabilities is proposed. The proposed prefetch scheme, along with the proposed storage cloud, is put under a two-fold evaluation in several deployment and usage scenarios in order to examine its performance with respect to the data transmission times, while also evaluating its outcomes compared to other state of the art solutions. The results show that the proposed solution shows significant improvement of the download speed when compared with the storage cloud, especially when large data are exchanged. In addition, the results of the proposed scheme evaluation depict that the proposed scheme improves the overall predictions, considering the coefficient of determination (R2 > 0.94) and the mean of errors (RMSE < 1), while also reducing the training data by 12%.

Electronic health records contain the patient's sensitive information. If these data are acquired by a malicious user, it will not only cause the pilferage of the patient's personal data but also affect the diagnosis and treatment. One of the most challenging tasks in cloud-based healthcare systems is to provide security and privacy to electronic health records. Various probabilistic data structures and watermarking techniques were used in the cloud-based healthcare systems to secure patient's data. Most of the existing studies focus on cuckoo and bloom filters, without considering their throughputs. In this research, a novel cloud security mechanism is introduced, which supersedes the shortcomings of existing approaches. The proposed solution enhances security with methods such as fragile watermark, least significant bit replacement watermarking, class reliability factor, and Morton filters included in the formation of the security mechanism. A Morton filter is an approximate set membership data structure (ASMDS) that proves many improvements to other data structures, such as cuckoo, bloom, semi-sorting cuckoo, and rank and select quotient filters. The Morton filter improves security; it supports insertions, deletions, and lookups operations and improves their respective throughputs by 0.9× to 15.5×, 1.3× to 1.6×, and 1.3× to 2.5×, when compared to cuckoo filters. We used Hadoop version 0.20.3, and the platform was Red Hat Enterprise Linux 6; we executed five experiments, and the average of the results has been taken. The results of the simulation work show that our proposed security mechanism provides an effective solution for secure data storage in cloud-based healthcare systems, with a load factor of 0.9. Furthermore, to aid cloud security in healthcare systems, we presented the motivation, objectives, related works, major research gaps, and materials and methods; we, thus, presented and implemented a cloud security mechanism, in the form of an algorithm and a set of results and conclusions. [ABSTRACT FROM AUTHOR]