The increasing adoption of transmission of medical images through internet in healthcare has led to several security threats to patient medical information. Permitting quiet data to be in peril may prompt hopeless harm, ethically and truly to the patient. Accordingly, it is important to take measures to forestall illicit access and altering of clinical pictures. This requests reception of security components to guarantee three fundamental security administrations – classification, content-based legitimacy, and trustworthiness of clinical pictures traded in telemedicine applications. Right now, inside created symmetric key cryptographic capacities are utilized. Pictorial model-based perceptual image hash is used to provide content-based authentication for malicious tampering detection and localization. The presentation of the projected algorithm has been evaluated using performance metrics such as PSNR, normalized correlation, entropy, and histogram analysis, and the simulation results show that the security services have been achieved effectively.
With increase in popularity of Cloud computing, most organizations are moving towards the Cloud. The main concern for these organizations when migrating to the Cloud is securing their data in the Cloud. There are security measures that can be deployed to address the risk the organization faces to the security threats posed within the Cloud. This project illustrates how the problem can be solved using data protection techniques and security analytics of the log data within the Cloud deployment. In PaaS implementation of Cloud, the customer and the Cloud vendor has a shared responsibility model and the project will discuss what customer can do for their responsibility in the areas highlighted above.Data is of paramount importance to any organization and protection of data becomes more complex in a Cloud offering as the storage is located off premise. Like any other environment devices, servers and applications in Cloud produce logs that can be aggregated and analyzed to identify security anomalies. Comparison of various log aggregation tools can give a detailed idea about what tool is better. Two log aggregation tools Splunk and the Elastic stack have been compared in this project. A combination of the above described strategies can address and point on various security risks, and help reduce the risk of the organization to a significant degree.
High-performance computing (HPC) applications require high-end computing systems, but not all scientists have access to such powerful systems. Cloud computing provides an opportunity to run these applications on the cloud without the requirement of investing in high-end parallel computing systems. We can analyze the performance of the HPC applications on private as well as public clouds. The performance of the workload on the cloud can be calculated using different benchmarking tools such as NAS parallel benchmarking and Rally. The workloads of HPC applications require use of many parallel computing systems to be run on a physical setup, but this facility is available on cloud computing environment without the need of investing in physical machines. We aim to analyze the ability of the cloud to perform well when running HPC workloads. We shall get the detailed performance of the cloud when running these applications on a private cloud and find the pros and cons of running HPC workloads on cloud environment.
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