With the rapid development of informatization, a vast amount of data is continuously generated and accumulated, leading to the emergence of cloud storage services. However, data stored in the cloud is beyond the control of users, posing various security risks. Cloud data auditing technology enables the inspection of data integrity in the cloud without the necessity of data downloading. Among these, public auditing schemes have experienced rapid development due to their ability to avoid additional user auditing expenses. However, malicious third-party auditors can compromise data privacy. This paper proposes an improved identity-based cloud auditing scheme that can resist malicious auditors. This scheme is also constructed on an identity-based public auditing scheme using blockchain to prevent malicious auditing. We found the scheme is not secure because a malicious cloud server can forge authentication tags for outsourced data blocks, while our scheme has not these security flaws. Through security proofs and performance analysis, we further demonstrate that our scheme is secure and efficient. Additionally, our scheme has typical application scenarios.

BACKGROUND: In the context of the telematics infrastructure, new data usage regulations, and the growing potential of artificial intelligence, cloud computing plays a key role in driving the digitalization in the German hospital sector.
METHODS: Against this background, the study aims to develop and validate a scale for assessing the cloud readiness of German hospitals. It uses the TPOM (Technology, People, Organization, Macro-Environment) framework to create a scoring system. A survey involving 110 Chief Information Officers (CIOs) from German hospitals was conducted, followed by an exploratory factor analysis and reliability testing to refine the items, resulting in a final set of 30 items.
RESULTS: The analysis confirmed the statistical robustness and identified key factors contributing to cloud readiness. These include IT security in the dimension \"technology\", collaborative research and acceptance for the need to make high quality data available in the dimension \"people\", scalability of IT resources in the dimension \"organization\", and legal aspects in the dimension \"macroenvironment\". The macroenvironment dimension emerged as particularly stable, highlighting the critical role of regulatory compliance in the healthcare sector.
CONCLUSIONS: The findings suggest a certain degree of cloud readiness among German hospitals, with potential for improvement in all four dimensions. Systemically, legal requirements and a challenging political environment are top concerns for CIOs, impacting their cloud readiness.

In human microbiome studies, mediation analysis has recently been spotlighted as a practical and powerful analytic tool to survey the causal roles of the microbiome as a mediator to explain the observed relationships between a medical treatment/environmental exposure and a human disease. We also note that, in a clinical research, investigators often trace disease progression sequentially in time; as such, time-to-event (e.g., time-to-disease, time-to-cure) responses, known as survival responses, are prevalent as a surrogate variable for human health or disease. In this paper, we introduce a web cloud computing platform, named as microbiome mediation analysis with survival responses (MiMedSurv), for comprehensive microbiome mediation analysis with survival responses on user-friendly web environments. MiMedSurv is an extension of our prior web cloud computing platform, named as microbiome mediation analysis (MiMed), for survival responses. The two main features that are well-distinguished are as follows. First, MiMedSurv conducts some baseline exploratory non-mediational survival analysis, not involving microbiome, to survey the disparity in survival response between medical treatments/environmental exposures. Then, MiMedSurv identifies the mediating roles of the microbiome in various aspects: (i) as a microbial ecosystem using ecological indices (e.g., alpha and beta diversity indices) and (ii) as individual microbial taxa in various hierarchies (e.g., phyla, classes, orders, families, genera, species). To illustrate its use, we survey the mediating roles of the gut microbiome between antibiotic treatment and time-to-type 1 diabetes. MiMedSurv is freely available on our web server ( http://mimedsurv.micloud.kr ).

暂无摘要。

The Internet of Vehicles (IoV) counts for much in advancing intelligent transportation by connecting people, vehicles, infrastructures, and cloud servers (CS). However, the open-access wireless channels within the IoV are susceptible to malicious attacks. Therefore, an authentication key agreement protocol becomes essential to ensure secure vehicular communications and protect vehicle privacy. Nevertheless, although the vehicles in the group are compromised, they can still update the group key and obtain the communication content in the existing group key agreement protocols. Therefore, it is still challenging to guarantee post-compromise forward security (PCFS). Dynamic key rotation is a common approach to realizing PCFS, which brings a heavy computation and communication burden. To address these issues, an efficient and robust continuous group key agreement (ER-CGKA) scheme with PCFS is designed for IoV. The propose-and-commit flow is employed to support asynchronous group key updates. Besides, the computation cost and communication overhead are significantly reduced based on the TreeKEM architecture. Furthermore, we adopt the threshold mechanism to resist the collusion attacks of malicious vehicles, which enhances the ER-CGKA scheme\'s robustness. Security analysis indicates that the proposed scheme satisfies all the fundamental security requirements of the IoV and achieves PCFS. The performance evaluation results show that our ER-CGKA scheme demonstrates a reduction in the computation cost of 18.82% (Client) and 33.18% (CS) approximately, and an increase in communication overhead of around 55.57% since pseudonyms are utilized to achieve conditional privacy-preserving. Therefore, our ER-CGKA scheme is secure and practical.

This paper outlines the protocol for the deployment of a cloud-based universal medical image repository system. The proposal aims not only at the deployment but also at the automatic expansion of the platform, incorporating Artificial Intelligence (AI) for the analysis of medical image examinations. The methodology encompasses efficient data management through a universal database, along with the deployment of various AI models designed to assist in diagnostic decision-making. By presenting this protocol, the goal is to overcome technical challenges and issues that impact all phases of the workflow, from data management to the deployment of AI models in the healthcare sector. These challenges include ethical considerations, compliance with legal regulations, establishing user trust, and ensuring data security. The system has been deployed, with a tested and validated proof of concept, possessing the capability to receive thousands of images daily and to sustain the ongoing deployment of new AI models to expedite the analysis process in medical image exams.

Task scheduling is a critical challenge in cloud computing systems, greatly impacting their performance. Task scheduling is a nondeterministic polynomial time hard (NP-Hard) problem that complicates the search for nearly optimal solutions. Five major uncertainty parameters, i.e., security, traffic, workload, availability, and price, influence task scheduling decisions. The primary rationale for selecting these uncertainty parameters lies in the challenge of accurately measuring their values, as empirical estimations often diverge from the actual values. The integral-valued Pythagorean fuzzy set (IVPFS) is a promising mathematical framework to deal with parametric uncertainties. The Dyna Q+ algorithm is the updated form of the Dyna Q agent designed specifically for dynamic computing environments by providing bonus rewards to non-exploited states. In this paper, the Dyna Q+ agent is enriched with the IVPFS mathematical framework to make intelligent task scheduling decisions. The performance of the proposed IVPFS Dyna Q+ task scheduler is tested using the CloudSim 3.3 simulator. The execution time is reduced by 90%, the makespan time is also reduced by 90%, the operation cost is below 50%, and the resource utilization rate is improved by 95%, all of these parameters meeting the desired standards or expectations. The results are also further validated using an expected value analysis methodology that confirms the good performance of the task scheduler. A better balance between exploration and exploitation through rigorous action-based learning is achieved by the Dyna Q+ agent.

Recent years, edge-cloud computing has attracted more and more attention due to benefits from the combination of edge and cloud computing. Task scheduling is still one of the major challenges for improving service quality and resource efficiency of edge-clouds. Though several researches have studied on the scheduling problem, there remains issues needed to be addressed for their applications, e.g., ignoring resource heterogeneity, focusing on only one kind of requests. Therefore, in this paper, we aim at providing a heterogeneity aware task scheduling algorithm to improve task completion rate and resource utilization for edge-clouds with deadline constraints. Due to NP-hardness of the scheduling problem, we exploit genetic algorithm (GA), one of the most representative and widely used meta-heuristic algorithms, to solve the problem considering task completion rate and resource utilization as major and minor optimization objectives, respectively. In our GA-based scheduling algorithm, a gene indicates which resource that its corresponding task is processed by. To improve the performance of GA, we propose to exploit a skew mutation operator where genes are associated to resource heterogeneity during the population evolution. We conduct extensive experiments to evaluate the performance of our algorithm, and results verify the performance superiority of our algorithm in task completion rate, compared with other thirteen classical and up-to-date scheduling algorithms.

HeXEHRS is a FHIR-based cloud EHR service designed to support healthcare in depopulated areas, powered by digital twin technology. Its core functionalities encompass standard EHR tasks including data exchange for healthcare processes. In the first year of this national project, we present the design and define the functionalities of the system.

STOP-HCV-HCC program to screen and treat hepatitis C, vaccinate for hepatitis B, and prevent hepatocellular carcinoma is implementing a cloud-based privacy-preserving platform to overcome electronic health record barriers to reporting, without data transfer, at four federally qualified health centers in South Texas, USA.