scholarly journals A Secured Data Processing Technique for Effective Utilization of Cloud Computing

2018 ◽  
Vol Special Issue on Scientific... ◽  
Author(s):  
Mbarek Marwan ◽  
Ali Kartit ◽  
Hassan Ouahmane
Keyword(s):  
Data Processing ◽  
Digital Humanities ◽  
Data Privacy ◽  
High Performance ◽  
Processing Technique ◽  
Cloud Services ◽  
It Services ◽  
Local Data ◽  
Sensitive Data ◽  
Analytical Tools

International audience 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.

scholarly journals Survey on Securing IoT Data using Homomorphic Encryption Scheme

2021 ◽  
Vol 10 (4) ◽  
pp. 76-81
Author(s):  
Anita Chaudhari ◽  
Rajesh Bansode
Keyword(s):  
Data Privacy ◽  
Homomorphic Encryption ◽  
Computational Cost ◽  
Computation Time ◽  
Cloud Services ◽  
Cloud Provider ◽  
Encryption Scheme ◽  
Sensitive Data ◽  
Cloud Data ◽  
Data User

In today’s world everyone is using cloud services. Every user uploads his/her sensitive data on cloud in encrypted form. If user wants to perform any type of computation on cloud data, user has to share credentials with cloud administrator. Which puts data privacy on risk. If user does not share his/her credentials with cloud provider, user has to download all data and only then decryption process and computation can be performed. This research, focuses on ECC based homomorphic encryption scheme is good by considering communication and computational cost. Many ECC based schemes are presented to provide data privacy. Analysis of different approaches has been done by selecting different common parameters. Based on the analysis minimum computation time is 0.25 Second required for ECC based homomorphic encryption (HE).

scholarly journals Trust Hardware Based Secured Privacy Preserving Computation System for Three-Dimensional Data

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1546
Author(s):  
Munan Yuan ◽  
Xiaofeng Li ◽  
Xiru Li ◽  
Haibo Tan ◽  
Jinlin Xu
Keyword(s):  
Data Privacy ◽  
High Performance ◽  
Distributed Storage ◽  
Three Dimensional ◽  
Privacy Preserving ◽  
Sensitive Data ◽  
Privacy And Security ◽  
Time Consumption ◽  
Blockchain Technology ◽  
3D Data

Three-dimensional (3D) data are easily collected in an unconscious way and are sensitive to lead biological characteristics exposure. Privacy and ownership have become important disputed issues for the 3D data application field. In this paper, we design a privacy-preserving computation system (SPPCS) for sensitive data protection, based on distributed storage, trusted execution environment (TEE) and blockchain technology. The SPPCS separates a storage and analysis calculation from consensus to build a hierarchical computation architecture. Based on a similarity computation of graph structures, the SPPCS finds data requirement matching lists to avoid invalid transactions. With TEE technology, the SPPCS implements a dual hybrid isolation model to restrict access to raw data and obscure the connections among transaction parties. To validate confidential performance, we implement a prototype of SPPCS with Ethereum and Intel Software Guard Extensions (SGX). The evaluation results derived from test datasets show that (1) the enhanced security and increased time consumption (490 ms in this paper) of multiple SGX nodes need to be balanced; (2) for a single SGX node to enhance data security and preserve privacy, an increased time consumption of about 260 ms is acceptable; (3) the transaction relationship cannot be inferred from records on-chain. The proposed SPPCS implements data privacy and security protection with high performance.

scholarly journals Memory-driven computing accelerates genomic data processing

2019 ◽  
Author(s):  
Matthias Becker ◽  
Milind Chabbi ◽  
Stefanie Warnat-Herresthal ◽  
Kathrin Klee ◽  
Jonas Schulte-Schrepping ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Data Processing ◽  
Data Privacy ◽  
Large Scale ◽  
Transcriptome Assembly ◽  
Primary Data ◽  
Attractive Alternative ◽  
Rna Seq ◽  
Local Data ◽  
Ngs Data

Next generation sequencing (NGS) is the driving force behind precision medicine and is revolutionizing most, if not all, areas of the life sciences. Particularly when targeting the major common diseases, an exponential growth of NGS data is foreseen for the next decades. This enormous increase of NGS data and the need to process the data quickly for real-world applications requires to rethink our current compute infrastructures. Here we provide evidence that memory-driven computing (MDC), a novel memory-centric hardware architecture, is an attractive alternative to current processor-centric compute infrastructures. To illustrate how MDC can change NGS data handling, we used RNA-seq assembly and pseudoalignment followed by quantification as two first examples. Adapting transcriptome assembly pipelines for MDC reduced compute time by 5.9-fold for the first step (SAMtools). Even more impressive, pseudoalignment by near-optimal probabilistic RNA-seq quantification (kallisto) was accelerated by more than two orders of magnitude with identical accuracy and indicated 66% reduced energy consumption. One billion RNA-seq reads were processed in just 92 seconds. Clearly, MDC simultaneously reduces data processing time and energy consumption. Together with the MDC-inherent solutions for local data privacy, a new compute model can be projected pushing large scale NGS data processing and primary data analytics closer to the edge by directly combining high-end sequencers with local MDC, thereby also reducing movement of large raw data to central cloud storage. We further envision that other data-rich areas will similarly benefit from this new memory-centric compute architecture.

scholarly journals Client/Server Model of Data Privacy using Extended Playfair Cipher for SaaS Applications on the Cloud

2019 ◽  
Vol 8 (10) ◽  
pp. 1933-1936 ◽  
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Data Privacy ◽  
Cloud Model ◽  
Cloud Services ◽  
Sensitive Data ◽  
The Public ◽  
Security Issues ◽  
On Demand ◽  
Self Service

Cloud Computing is a paradigm of distributed computing that delivers on-demand and utility-based services to its customers. It provides a set of shared computing resources such as networking, servers, storage, and applications in the form of services to an organization or an individual. The major benefits of cloud computing include on-demand self-service and cost-effectiveness. For the customer, there is no up-front cost for setting up and running the applications on the cloud. Despite the benefits provided by various cloud services, the outsourcing of data storage and computation raise many new security issues. One of such security issues that have to be addressed before uploading our sensitive data to the cloud is data privacy. With the cloud model, end-users lose control over the physical location of data, because data will be stored and processed elsewhere on the globe and not in the local computer. Hence, we need an algorithm for encrypting the data that can be stored and retrieved from a database managed by the public cloud.

scholarly journals Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Qi Dou ◽  
Tiffany Y. So ◽  
Meirui Jiang ◽  
Quande Liu ◽  
Varut Vardhanabhuti ◽  
...  
Keyword(s):  
Data Privacy ◽  
Medical Training ◽  
External Validation ◽  
Mainland China ◽  
Privacy Preserving ◽  
Generalization Capability ◽  
Major Focus ◽  
Sensitive Data ◽  
Lung Abnormalities ◽  
Medical Image Diagnosis

AbstractData privacy mechanisms are essential for rapidly scaling medical training databases to capture the heterogeneity of patient data distributions toward robust and generalizable machine learning systems. In the current COVID-19 pandemic, a major focus of artificial intelligence (AI) is interpreting chest CT, which can be readily used in the assessment and management of the disease. This paper demonstrates the feasibility of a federated learning method for detecting COVID-19 related CT abnormalities with external validation on patients from a multinational study. We recruited 132 patients from seven multinational different centers, with three internal hospitals from Hong Kong for training and testing, and four external, independent datasets from Mainland China and Germany, for validating model generalizability. We also conducted case studies on longitudinal scans for automated estimation of lesion burden for hospitalized COVID-19 patients. We explore the federated learning algorithms to develop a privacy-preserving AI model for COVID-19 medical image diagnosis with good generalization capability on unseen multinational datasets. Federated learning could provide an effective mechanism during pandemics to rapidly develop clinically useful AI across institutions and countries overcoming the burden of central aggregation of large amounts of sensitive data.

scholarly journals HPC Cloud Architecture to Reduce HPC Workflow Complexity in Containerized Environments

2021 ◽  
Vol 11 (3) ◽  
pp. 923
Author(s):  
Guohua Li ◽  
Joon Woo ◽  
Sang Boem Lim
Keyword(s):  
High Performance ◽  
Cloud Services ◽  
Workload Management ◽  
Job Management ◽  
Security Issues ◽  
Cloud Architecture ◽  
Management Efficiency ◽  
Complexity Problem ◽  
And Performance ◽  
Hpc Cloud

The complexity of high-performance computing (HPC) workflows is an important issue in the provision of HPC cloud services in most national supercomputing centers. This complexity problem is especially critical because it affects HPC resource scalability, management efficiency, and convenience of use. To solve this problem, while exploiting the advantage of bare-metal-level high performance, container-based cloud solutions have been developed. However, various problems still exist, such as an isolated environment between HPC and the cloud, security issues, and workload management issues. We propose an architecture that reduces this complexity by using Docker and Singularity, which are the container platforms most often used in the HPC cloud field. This HPC cloud architecture integrates both image management and job management, which are the two main elements of HPC cloud workflows. To evaluate the serviceability and performance of the proposed architecture, we developed and implemented a platform in an HPC cluster experiment. Experimental results indicated that the proposed HPC cloud architecture can reduce complexity to provide supercomputing resource scalability, high performance, user convenience, various HPC applications, and management efficiency.

scholarly journals 3D Simulation-Based Acoustic Wave Resonator Analysis and Validation Using Novel Finite Element Method Software

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2715
Author(s):  
Ruth Yadira Vidana Morales ◽  
Susana Ortega Cisneros ◽  
Jose Rodrigo Camacho Perez ◽  
Federico Sandoval Ibarra ◽  
Ricardo Casas Carrillo
Keyword(s):  
Finite Element ◽  
High Performance ◽  
Cloud Services ◽  
3D Simulation ◽  
3D Simulations ◽  
Acoustic Resonators ◽  
Analysis And Design ◽  
Wave Resonator ◽  
Film Bulk ◽  
Simulation Results

This work illustrates the analysis of Film Bulk Acoustic Resonators (FBAR) using 3D Finite Element (FEM) simulations with the software OnScale in order to predict and improve resonator performance and quality before manufacturing. This kind of analysis minimizes manufacturing cycles by reducing design time with 3D simulations running on High-Performance Computing (HPC) cloud services. It also enables the identification of manufacturing effects on device performance. The simulation results are compared and validated with a manufactured FBAR device, previously reported, to further highlight the usefulness and advantages of the 3D simulations-based design process. In the 3D simulation results, some analysis challenges, like boundary condition definitions, mesh tuning, loss source tracing, and device quality estimations, were studied. Hence, it is possible to highlight that modern FEM solvers, like OnScale enable unprecedented FBAR analysis and design optimization.

A Novel Cloud-Assisted Secure Deep Feature Classification Framework for Cancer Histopathology Images

2021 ◽  
Vol 21 (2) ◽  
pp. 1-22
Author(s):  
Abhinav Kumar ◽  
Sanjay Kumar Singh ◽  
K Lakshmanan ◽  
Sonal Saxena ◽  
Sameer Shrivastava
Keyword(s):  
Differential Privacy ◽  
Model Performance ◽  
Healthcare Services ◽  
Cloud Services ◽  
Efficient Computation ◽  
Sensitive Data ◽  
Classification Framework ◽  
Proposed Model ◽  
Diagnostic Framework ◽  
Histopathological Image Classification

The advancements in the Internet of Things (IoT) and cloud services have enabled the availability of smart e-healthcare services in a distant and distributed environment. However, this has also raised major privacy and efficiency concerns that need to be addressed. While sharing clinical data across the cloud that often consists of sensitive patient-related information, privacy is a major challenge. Adequate protection of patients’ privacy helps to increase public trust in medical research. Additionally, DL-based models are complex, and in a cloud-based approach, efficient data processing in such models is complicated. To address these challenges, we propose an efficient and secure cancer diagnostic framework for histopathological image classification by utilizing both differential privacy and secure multi-party computation. For efficient computation, instead of performing the whole operation on the cloud, we decouple the layers into two modules: one for feature extraction using the VGGNet module at the user side and the remaining layers for private prediction over the cloud. The efficacy of the framework is validated on two datasets composed of histopathological images of the canine mammary tumor and human breast cancer. The application of differential privacy preserving to the proposed model makes the model secure and capable of preserving the privacy of sensitive data from any adversary, without significantly compromising the model accuracy. Extensive experiments show that the proposed model efficiently achieves the trade-off between privacy and model performance.

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