Cloud-based E-health Systems Research Articles

A hybrid optimization approach for securing cloud-based e-health systems

A hybrid optimization approach for securing cloud-based e-health systems

Fischer machine learning for mobile cloud computing in eHealth systems using blockchain mechanism

The Electronic Healthcare (eHealth) systems are competent to ensure effective care engineering and intensified healthcare quality which are user-friendly cache and administration, in Electronic Health Records (EHRs). For secure EHRs of Mobile Cloud-based eHealth systems, ensuring high security and data privacy, Interplanetary File System in healthcare has traditionally been concentrated. However, there has been a recent push towards achieving high quality of e-health services because blockchain-based health care applications require QoS guarantees in terms of requirements such as network latency and end-to-end delay. In this work, an Extended Validation Certification-based Fischer Neural Network Optimization (EVC-FNNO) method for secured Mobile Cloud-based E-Health Systems is proposed. With the identity being the digital certificate, the EVC is provided with the identity to the mobile cloud user who will transact in the network. In this way, the mobile cloud user is being ensured to access the ledger for the transaction. Therefore, both data privacy and security is said to be provided. Next, with Fischer Neural Network Optimization (FNNO), every authenticated mobile cloud user via EVC then possess a copy of shared ledger, therefore resolving data acquisition in cloud server and hence solving network latency. The proposed method is verified by some demonstrative examples in addressing QoS. The empirical results show that the EVC-FNNO method provides an efficient solution by validating the mobile cloud user sensitive health information with digital certificate. Security analysis proves that the EVC-FNNO method is secure. We also conduct comprehensive performance evaluations that demonstrate the high efficiency of the EVC-FNNO method in terms of end-to-end delay, network latency and data privacy, compared to the existing data sharing methods.

An integrating computing framework based on edge-fog-cloud for internet of healthcare things applications

History has demonstrated that healthcare and medical systems play a crucial role in enforcing the development of science and technology. Humans have been seeing an explosive growth of e-health applications, where cloud computing has dominated e-healthcare systems and all domains in the past decades. However, one primary barrier to cloud-based e-health systems is their high response time. In emergencies, data aggregation and handling and decision-making regarding treatment need to be performed in seconds and directly affect the life of patients. These issues relate to resource constraints, such as medical equipment, ambulances, medical staff, medical facilities, and other conditions. To address these problems, computing architectures have been introduced, such as edge, fog, and cloud computing methods. In this work, we propose an all-in-one computing framework based on combining different computing solutions in real-time smart healthcare domains. Then, considering the efficiency of the proposed solution by the queue network model tool, we give recommendations for the optimal network infrastructure deployment depending on the scale-patients of hospitals. We believe that the results of this work will play an important role in the network infrastructure optimization of medical facilities while ensuring the provision of real-time healthcare services.

Streamlining Compliance and Governance with AI in Cloud-based Programs

Cloud-based programs offer numerous advantages to businesses and governments, including cost reduction, scalability, and on-demand services. Software as a Service (SaaS) is a prominent cloud computing model, providing potential cost-savings and improved cost control. Cloud-based programs enable efficient resource provisioning, auto-scaling, and dynamic resource acquisition and release, leading to increased profitability and reduced reliance on on-site IT infrastructure. However, adopting cloud-based programs comes with challenges related to security and compliance. This article explores the advantages and differences of cloud-based programs compared to traditional software. It highlights the benefits of using AI in cloud-based programs to improve governance and compliance by streamlining processes and automating tasks. AI can enhance energy efficiency in cloud-based programs and significantly contribute to cost savings. However, it also introduces potential security risks and the need for robust security solutions to protect data in cloud-based eHealth systems. The article emphasizes best practices for ensuring security in cloud-based programs, such as role-based access, encryption, and data monitoring. It discusses the potential risks of using AI for security and the importance of developing governance practices specific to AI applications. Moreover, it explores the benefits of AI for compliance and governance in cloud-based programs, such as risk management, scalability, and improved efficiency. The article underscores the long-term benefits of using AI in compliance and governance, including the enhancement of corporate governance and expertise in AI risks over time. In conclusion, cloud-based programs coupled with AI present opportunities to revolutionize various industries by providing cost-effective, scalable, and secure solutions for compliance and governance needs. However, adopting AI in these programs requires careful consideration of security measures and adherence to specific AI governance practices to ensure the responsible and beneficial deployment of AI technologies.

Analysis of Privacy and Security Challenges in e-Health Clouds

Electronic Health Records (EHR) techniques are being used at an increasingly faster rate to store the patient data making it easier to retrieve, share and utilize it efficiently. This data can be used for research purposes, clinical trials and for studying epidemiology to come up with strategies for epidemic control. With a huge global inflation, the increasing costs of healthcare and the shortage of medicine, it becomes convenient for the healthcare organizations to migrate from the traditional healthcare system to a more sophisticated, cost effective and efficient cloud-based e-Health model. To optimize the full potential of an ICT-based e-Health system, it is imperative for the existing healthcare systems to be implemented in a full-fledged cloud environment. However, with numerous benefits of technology, it might pose some privacy and security threats as well. Therefore, the security and access control of such information is of vital significance. Nonetheless, with the increasing interest of healthcare organizations to migrate from the conventional healthcare systems to the modern cloud-based e-Health systems, not much care is being taken to address security and privacy issues effectively towards the protection of sensitive data.

Toward Secure Storage in Cloud-based eHealth Systems: A Blockchain-Assisted Approach

Cloud-based eHealth systems enable patients and medical institutions to manage their EHRs in a flexible and efficient way. Since EHRs are the most sensitive data for people and have significant value to adversaries, outsourced EHRs suffer from both internal and external threats. In this article, we first identify the threats toward the security of outsourced EHRs and present system and security criteria that a secure cloud-based eHealth system should achieve. Then we provide a blockchain-assisted eHealth framework for securing outsourced EHRs that meets the identified criteria. We also show a case study where the blockchain technology is applied to ensure the security of outsourced EHRs. Finally, we discuss several promising issues and research directions on blockchain-assisted secure EHRs in cloud-based eHealth systems.

Collaborative Ehealth Privacy and Security: An Access Control With Attribute Revocation Based on OBDD Access Structure.

The digitization of health records due to technological developments has paved the way for patients to be collaboratively treated by different healthcare institutions. In collaborative ehealth systems, a patient's health data is stored remotely in the cloud for sharing with different healthcare service providers. However, the use of third parties for storage exposes the data to several privacy and security violation threats. Ciphertext policy attribute-based encryption (CP-ABE) which provides a fine-grained access control is a promising solution to privacy and security issues in the cloud environment and as a result, it has been widely studied for secure sharing of health data in cloud-based ehealth systems. Addressing the aspects of expressiveness, efficiency, user collusion resistance and attribute/user revocation in CP-ABE have been at the forefront of these studies. Thus, in this article, we proposed a novel expressive, efficient and collusion-resistant access control scheme with immediate attribute/user revocation for secure sharing of health data in collaborative ehealth systems. The proposed scheme additionally achieves forward and backward security. To realize these features, our access control is based on the ordered binary decision diagram (OBDD) access structure and it binds the user keys to the user identities. Security and performance analysis show that our proposed scheme is secure, expressive and efficient.

Privacy-preserving range query over multi-source electronic health records in public clouds

Range query is an important data search technique in cloud-based electronic healthcare (eHealth) systems. It enables authorized doctors to retrieve target electronic health records (EHRs) that are generated and outsourced by patients from the cloud server. In reality, patients always encrypt their EHRs before outsourcing, making the range query impossible. In this paper, we identify three threats in real cloud-based eHealth systems, i.e., privacy leakage, frequency analysis, and identical data inference. To capture the security properties that resist these threats, we define a security notion of indistinguishability under multi-source ordered chosen plaintext attack (IND-MSOCPA). Then, we propose a multi-source order-preserving encryption (MSOPE) scheme for cloud-based eHealth systems to enable range queries over encrypted EHRs from multiple patients. Security analysis proves that the MSOPE scheme is IND-MSOCPA secure. We also conduct comprehensive performance evaluations, which demonstrate the high efficiency of the MSOPE scheme.

A dual privacy-preservation scheme for cloud-based eHealth systems

With the development of wireless sensor network and internet, ubiquitous eHealth care systems are booming. However, a great deal of data computing and storage processing restrict the scalability of ubiquitous eHealth. As a solution, cloud-based eHealth is coming up. However, using cloud-computing model often causes some privacy concerns. Massive medical data in eHealth system contain numerous sensitive information and electronic health records of users. Meanwhile, if data encryption is adopted to protect privacy, cloud center cannot process data. To solve this problem, a scheme based on fully homomorphism conception for privacy protection and data processing of eHealth is proposed in this paper. Our scheme not only meets the aforementioned dual requirements but also limit the arbitrary actions of patients and doctors.

Cost-effective resource provisioning for multimedia cloud-based e-health systems

Recently, multimedia cloud is being considered as a new effective serving mode in e-Health area that meets the requirement of scalable and economic multimedia service for e-health. It can provide a flexible stack of powerful Virtual Machine (VM) resources of cloud like CPU, memory, storage, network bandwidth etc. on demand to manage e-health media services and applications (e.g. medical image/video retrieval, health video transcoding, streaming, video rendering, sharing and delivery) at lower cost. However, one major issue here is how to efficiently allocate VM resources dynamically based on e-health applications' QoS demands and support energy and cost savings by optimizing the number of servers in use. In order to solve this problem, we propose a cost effective and dynamic VM allocation model based on Nash bargaining solution. With extensive simulations it is shown that the proposed mechanism can reduce the overall cost of running servers while at the same time guarantee QoS demand and maximize resource utilization in various dimensions of server resources.