A novel approach based on segmentation for securing medical image processing over cloud

International audienceHealthcare professionals require advanced image processing software to enhance the quality of clinical decisions. However, any investment in sophisticated local applications would dramatically increase healthcare costs. To address this issue, medical providers are interested in adopting cloud technology. In spite of its multiple advantages, outsourcing computations to an external provider arises several challenges. In fact, security is the major factor hindering the widespread acceptance of this new concept. Recently, various solutions have been suggested to fulfill healthcare demands. Though, ensuring privacy and high performance needs more improvements to meet the healthcare sector requirements. To this end, we propose a framework based on segmentation approach to secure cloud-based medical image processing in the healthcare system

A Secured Data Processing Technique for Effective Utilization of Cloud Computing

Digital humanities require IT Infrastructure and sophisticated analytical tools, including datavisualization, data mining, statistics, text mining and information retrieval. Regarding funding, tobuild a local data center will necessitate substantial investments. Fortunately, there is another optionthat will help researchers take advantage of these IT services to access, use and share informationeasily. Cloud services ideally offer on-demand software and resources over the Internet to read andanalyze ancient documents. More interestingly, billing system is completely flexible and based onresource usage and Quality of Service (QoS) level. In spite of its multiple advantages, outsourcingcomputations to an external provider arises several challenges. Specifically, security is the majorfactor hindering the widespread acceptance of this new concept. As a case study, we review the use ofcloud computing to process digital images safely. Recently, various solutions have been suggested tosecure data processing in cloud environement. Though, ensuring privacy and high performance needsmore improvements to protect the organization's most sensitive data. To this end, we propose aframework based on segmentation and watermarking techniques to ensure data privacy. In this respect,segementation algorithm is used to to protect client's data against untauhorized access, whilewatermarking method determines and maintains ownership. Consequentely, this framework willincrease the speed of development on ready-to-use digital humanities tools

A novel approach based on segmentation for securing medical image processing over cloud

Healthcare professionals require advanced image processing software to enhance the quality of clinical decisions. However, any investment in sophisticated local applications would dramatically increase healthcare costs. To address this issue, medical providers are interested in adopting cloud technology. In spite of its multiple advantages, outsourcing computations to an external provider arises several challenges. In fact, security is the major factor hindering the widespread acceptance of this new concept. Recently, various solutions have been suggested to fulfill healthcare demands. Though, ensuring privacy and high performance needs more improvements to meet the healthcare sector requirements. To this end, we propose a framework based on segmentation approach to secure cloud-based medical image processing in the healthcare system

A Secured Data Processing Technique for Effective Utilization of Cloud Computing

Digital humanities require IT Infrastructure and sophisticated analytical tools, including datavisualization, data mining, statistics, text mining and information retrieval. Regarding funding, tobuild a local data center will necessitate substantial investments. Fortunately, there is another optionthat will help researchers take advantage of these IT services to access, use and share informationeasily. Cloud services ideally offer on-demand software and resources over the Internet to read andanalyze ancient documents. More interestingly, billing system is completely flexible and based onresource usage and Quality of Service (QoS) level. In spite of its multiple advantages, outsourcingcomputations to an external provider arises several challenges. Specifically, security is the majorfactor hindering the widespread acceptance of this new concept. As a case study, we review the use ofcloud computing to process digital images safely. Recently, various solutions have been suggested tosecure data processing in cloud environement. Though, ensuring privacy and high performance needsmore improvements to protect the organization's most sensitive data. To this end, we propose aframework based on segmentation and watermarking techniques to ensure data privacy. In this respect,segementation algorithm is used to to protect client's data against untauhorized access, whilewatermarking method determines and maintains ownership. Consequentely, this framework willincrease the speed of development on ready-to-use digital humanities tools

A Cloud Solution for Securing Medical Image Storage

Cloud computing is an easy-to-use, affordable solution to manage and analyze medical data. Therefore, this paradigm has gained wide acceptance in the healthcare sector as a cost-efficient way for a successful Electronic Medical Records (EMR) implementation. Cloud technology is, however, subject to increasing criticism because of the numerous security vulnerabilities. In this regard, we propose a framework to protect confidential data through the development of new security measures, including compression, secret share scheme and XOR operation. The primary objective of the proposal is to achieve the right balance between security and usability. To this aim, we divide an image into several blocks and then encrypt each piece separately with different cryptographic keys. To enhance privacy and performance, we suggest DepSky architecture to keep data on various storage nodes. Simulation experiments have been conducted to prove the effectiveness of the proposed methodology

A Cloud Solution for Securing Medical Image Storage

Cloud computing is an easy-to-use, affordable solution to manage and analyze medical data. Therefore, this paradigm has gained wide acceptance in the healthcare sector as a cost-efficient way for a successful Electronic Medical Records (EMR) implementation. Cloud technology is, however, subject to increasing criticism because of the numerous security vulnerabilities. In this regard, we propose a framework to protect confidential data through the development of new security measures, including compression, secret share scheme and XOR operation. The primary objective of the proposal is to achieve the right balance between security and usability. To this aim, we divide an image into several blocks and then encrypt each piece separately with different cryptographic keys. To enhance privacy and performance, we suggest DepSky architecture to keep data on various storage nodes. Simulation experiments have been conducted to prove the effectiveness of the proposed methodology

Leveraging artificial intelligence and mutual authentication to optimize content caching in edge data centers

Available online Edge data centers are designed to meet the stringent QoE requirements of delay-sensitive and computationally intensive services in Content Delivery Network (CDN) and 5G networks. The primary purpose of this paper was to formulate and solve the problem of optimizing many control variables jointly: (i) what contents to store by taking into consideration edge capacity, and (ii) what contents to recommend to each Internet of Everything (IoE) item, based on identity and access management (IAM). In reactive caching policy, we proposed a new Two-Factor Authentication (2FA) scheme founded upon the Elliptic Curve Cryptography (ECC) and one-way hash function for access control. More interestingly, we use Non-negative Matrix Factorization (NMF), Fuzzy C-Means (FCM), Random Forest (RF) and Pearson Correlation (PC) to improve the accuracy and latency of traditional data filtering models. The intelligent recommendation engine we propose is designed to be implemented by cloud for caching and prefetching contents at the edge. The experimental results validate the theoretical guarantees of the proposed solution and its ability to achieve significant performance gains compared to common baseline models