scholarly journals Enhanced Framework for Ensuring Privacy Preserving Image Retrieval in Cloud

2019 ◽  
Vol 8 (12) ◽  
pp. 2127-2132
Keyword(s):  
Data Storage ◽  
Large Scale ◽  
Research Work ◽  
Image Data ◽  
Cloud Service ◽  
Sensitive Information ◽  
Cloud Platform ◽  
Privacy And Security ◽  
Computing Platform ◽  
Computing Platforms

Large scale of images data sets are being produced every day by various digital devices. Due to huge computational jobs make people seizure to cloud platforms for their efficient & economical reckoning resources. These computing platforms in which assets are provided as services of the internet. Sensitive information stored in cloud makes more challenging in data security and access control. Once data is uploaded to cloud-platform, the privacy and security of image-data fully depend and believe upon cloud service provider honesty. Our proposed work deals with securing image where high protections are applied on multimedia contents. This paper deals with studies security challenges algorithms lies in image at the time of constructing cloud platform. In this a new enhanced security technique investigated, includes secure by using computation and encryption, act as a security information guard for high secrecy in cloud platform data storage areas. In our research work, cipher-text image is created and performing encryption-decryption at User level. Data hiding and ECC (Elliptic curve cryptosystem) based watermarking technique at cloud computing platform.

scholarly journals Digitalization of Business Processes of Enterprises of the Ecosystem of Industry 4.0: Virtual-Real Aspect of Economic Growth Reserves

2021 ◽  
Vol 18 ◽  
pp. 569-580
Author(s):  
Kateryna Kraus ◽  
Nataliia Kraus ◽  
Oleksandr Manzhura
Keyword(s):  
Economic Growth ◽  
Virtual Reality ◽  
Data Storage ◽  
Industry 4.0 ◽  
Large Scale ◽  
Business Processes ◽  
Digital Transformation ◽  
Cloud Services ◽  
Scale Production ◽  
Cloud Platform

The purpose of the research is to present the features of digitization of business processes in enterprises as a foundation on which the gradual formation of Industry 4.0 and the search for economic growth in new virtual reality, which has every chance to be a decisive step in implementing digital strategy for Ukraine and development of the innovation ecosystem. Key problems that arise during the digitalization of business processes in enterprises are presented, among which are: the historical orientation of production to mass, “running” sizes and large batches; large-scale production load; the complexity of cooperation and logic between production sites. It is determined that high-quality and effective tools of innovation-digital transformation in the conditions of virtual reality should include: a single system of on-line order management for all enterprises (application registration – technical expertise – planning – performance control – shipment); Smart Factory, Predictive Maintenance, IIoT, CRM, SCM. Features of digital transformation in the part of formation of enterprises of the ecosystem of Industry 4.0 are revealed. The capabilities and benefits of using Azure cloud platform in enterprises, which includes more than 200 products and cloud services, are analyzed. Azure is said to support open source technologies, so businesses have the ability to use tools and technologies they prefer and are more useful. After conducting a thorough analysis of the acceleration of deep digitalization of business processes by enterprises, authors proposed to put into practice Aruba solution for tracking contacts in the fight against COVID-19. Aruba technology helps locate, allowing you to implement flexible solutions based on Aruba Partner Ecosystem using a USB interface. It is proposed to use SYNTEGRA – a data integration service that provides interactive analytics and provides data models and dashboards in order to accelerate the modernization of data storage and management, optimize reporting in the company and obtain real-time analytics. The possibilities of using Azure cloud platform during the digitization of business processes of enterprises of the ecosystem of Industry 4.0 in the conditions of virtual reality are determined.

scholarly journals Improvement of Privacy and Security in Hybrid Cloud with Attribute Group Based Access Control

2019 ◽  
pp. 57-61
Author(s):  
Kayalvili S ◽  
Sowmitha V
Keyword(s):  
Cloud Computing ◽  
Access Control ◽  
Data Storage ◽  
Service Providers ◽  
Cloud Service ◽  
Security Requirements ◽  
Security And Privacy ◽  
Data Outsourcing ◽  
Privacy And Security ◽  
Control Approach

Cloud computing enables users to accumulate their sensitive data into cloud service providers to achieve scalable services on-demand. Outstanding security requirements arising from this means of data storage and management include data security and privacy. Attribute-based Encryption (ABE) is an efficient encryption system with fine-grained access control for encrypting out-sourced data in cloud computing. Since data outsourcing systems require flexible access control approach Problems arises when sharing confidential corporate data in cloud computing. User-Identity needs to be managed globally and access policies can be defined by several authorities. Data is dual encrypted for more security and to maintain De-Centralization in Multi-Authority environment.

Study and Survey on Blockchain Privacy and Security Issues

2021 ◽  
pp. 169-191
Author(s):  
Sourav Banerjee ◽  
Debashis Das ◽  
Manju Biswas ◽  
Utpal Biswas
Keyword(s):  
Data Storage ◽  
Security And Privacy ◽  
Sensitive Information ◽  
Privacy And Security ◽  
Unauthorized Access ◽  
Security Issues ◽  
Blockchain Technology ◽  
Targeted Attacks ◽  
Wide Range ◽  
Financial Transactions

Blockchain-based technology is becoming increasingly popular and is now used to solve a wide range of tasks. And it's not all about cryptocurrencies. Even though it's based on secure technology, a blockchain needs protection as well. The risks of exploits, targeted attacks, or unauthorized access can be mitigated by the instant incident response and system recovery. Blockchain technology relies on a ledger to keep track of all financial transactions. Ordinarily, this kind of master ledger would be a glaring point of vulnerability. Another tenet of security is the chain itself. Configuration flaws, as well as insecure data storage and transfers, may cause leaks of sensitive information. This is even more dangerous when there are centralized components within the platform. In this chapter, the authors will demonstrate where the disadvantages of security and privacy in blockchain are currently and discuss how blockchain technology can improve these disadvantages and outlines the requirements for future solution.

The Design and Implementation of Intelligent Community Monitoring System

2012 ◽  
Vol 463-464 ◽  
pp. 1701-1705
Author(s):  
Yu Lan Wang ◽  
Jian Xiong Wang ◽  
Yao Hui Li
Keyword(s):  
Data Storage ◽  
Research Work ◽  
Image Data ◽  
Moving Object ◽  
Video Image ◽  
Security Monitoring ◽  
Community Monitoring ◽  
Database Compression ◽  
Intelligent Community ◽  
Intelligent Security

This paper designs a stable, efficient and intelligent video surveillance system, which is based on the review and analysis of the domestic and internation. And it is related research work on the basis of the intelligent security monitoring. The system is used by the Web server and database server model, and it can detect the moving object in scene of monitor. Firstly this paper analyzes the structure of server system, it uses B/S, database, ActiveX technology and is completed finally. Secondly this paper realizes the video image data in bulk storage, read, update and maintenance by using the database. The bmp format will be converted to JPG format effectively to realize the image database compression. It is valued to the video image data storage and management. Finally, the accuracy and response time can be improved in moving object detection, which is based on background subtraction method

scholarly journals A Secure IoT-Based Cloud Platform Selection Using Entropy Distance Approach and Fuzzy Set Theory

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Alakananda Chakraborty ◽  
Muskan Jindal ◽  
Mohammad R. Khosravi ◽  
Prabhishek Singh ◽  
Achyut Shankar ◽  
...  
Keyword(s):  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Large Scale ◽  
Service Providers ◽  
Cost Optimization ◽  
Cloud Service ◽  
Security And Privacy ◽  
Cloud Platform ◽  
Efficiency Cost

With the growing emergence of the Internet connectivity in this era of Gen Z, several IoT solutions have come into existence for exchanging large scale of data securely, backed up by their own unique cloud service providers (CSPs). It has, therefore, generated the need for customers to decide the IoT cloud platform to suit their vivid and volatile demands in terms of attributes like security and privacy of data, performance efficiency, cost optimization, and other individualistic properties as per unique user. In spite of the existence of many software solutions for this decision-making problem, they have been proved to be inadequate considering the distinct attributes unique to individual user. This paper proposes a framework to represent the selection of IoT cloud platform as a MCDM problem, thereby providing a solution of optimal efficacy with a particular focus in user-specific priorities to create a unique solution for volatile user demands and agile market trends and needs using optimized distance-based approach (DBA) aided by Fuzzy Set Theory.

scholarly journals Balancing Privacy Vs Efficiency in Data Analytics using Nearest Neighbour Randomization

2019 ◽  
Vol 8 (12) ◽  
pp. 2289-2295
Keyword(s):  
Data Analytics ◽  
Large Scale ◽  
Personal Data ◽  
Privacy And Security ◽  
Computing Platform ◽  
Use Of Data ◽  
Digital Era ◽  
Considerable Research ◽  
Group Data ◽  
Security Practices

The Digital era marked by the unrivalled growth of Internet and its services with day-to-day technological advancements has paved way for a data driven society. This digital explosion offers opportunities for extracting valuable information from collected data, which are used by organizations and research establishments for synergistic advantage. However, privacy of online divulged data is an issue that gets overlooked as a consequence of such large-scale analytics. Although, privacy and security practices conjointly determine the ethics of data collection and its use, personal data of individuals is largely at risk of disclosure. Considerable research has gone into privacy preserving analytics, in the light of Big Data and IoT boom, but scalable and efficient techniques, that do not compromise the usefulness of privacy constrained data, continues to be a challenging arena for research. The proposed work makes use of a distance-based perturbation method to group data and further randomizes data. The efficacy of perturbed data is evaluated for classification task that gives results on par with the non-perturbed counterpart. The relative performance of the algorithm is also evaluated on the parallel computing platform Spark. Results show that the technique does not hinder the use of data for holistic analysis while privacy is subjectively maintained.

scholarly journals Contextual Contracts for Component-Based Resource Abstraction in a Cloud of HPC Services

2019 ◽  
Author(s):  
Wagner Al Alam ◽  
Francisco Carvalho Junior
Keyword(s):  
Cloud Computing ◽  
Parallel Computing ◽  
Large Scale ◽  
Matrix Multiplication ◽  
Small Scale ◽  
Computing Systems ◽  
Computing Platform ◽  
Computing Platforms

The efforts to make cloud computing suitable for the requirements of HPC applications have motivated us to design HPC Shelf, a cloud computing platform of services for building and deploying parallel computing systems for large-scale parallel processing. We introduce Alite, the system of contextual contracts of HPC Shelf, aimed at selecting component implementations according to requirements of applications, features of targeting parallel computing platforms (e.g. clusters), QoS (Quality-of-Service) properties and cost restrictions. It is evaluated through a small-scale case study employing a componentbased framework for matrix-multiplication based on the BLAS library.

scholarly journals AN EFFICIENT APPROACH TO IMPLEMENT FEDERATED CLOUDS

2017 ◽  
Vol 10 (13) ◽  
pp. 40
Author(s):  
Pappu Sowmya ◽  
R Kumar
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Low Cost ◽  
Cloud Service ◽  
Cloud Services ◽  
The Internet ◽  
Security Model ◽  
Privacy And Security ◽  
Security Issues ◽  
Internet Users

Cloud computing is one of the trending technologies that provide boundless virtualized resources to the internet users as an important services through the internet, while providing the privacy and security. By using these cloud services, internet users get many parallel computing resources at low cost. It predicted that till 2016, revenues from the online business management spent $4 billion for data storage. Cloud is an open source platform structure, so it is having more chances to malicious attacks. Privacy, confidentiality, and security of stored data are primary security challenges in cloud computing. In cloud computing, ‘virtualization’ is one of the techniques dividing memory into different blocks. In most of the existing systems there is only single authority in the system to provide the encrypted keys. To fill the few security issues, this paper proposed a novel authenticated trust security model for secure virtualization system to encrypt the files. The proposed security model achieves the following functions: 1) allotting the VSM(VM Security Monitor) model for each virtual machine; 2) providing secret keys to encrypt and decrypt information by symmetric encryption.The contribution is a proposed architecture that provides a workable security that a cloud service provider can offer to its consumers. Detailed analysis and architecture design presented to elaborate security model.

scholarly journals DESIGN AND VERIFICATION OF REMOTE SENSING IMAGE DATA CENTER STORAGE ARCHITECTURE BASED ON HADOOP

2018 ◽  
Vol XLII-3 ◽  
pp. 1639-1642
Author(s):  
D. Tang ◽  
X. Zhou ◽  
Y. Jing ◽  
W. Cong ◽  
C. Li
Keyword(s):  
Remote Sensing ◽  
Data Storage ◽  
Data Center ◽  
High Speed ◽  
Large Scale ◽  
Distributed Storage ◽  
Image Data ◽  
Remotely Sensed Data ◽  
Processing Technologies ◽  
Data Parallel

The data center is a new concept of data processing and application proposed in recent years. It is a new method of processing technologies based on data, parallel computing, and compatibility with different hardware clusters. While optimizing the data storage management structure, it fully utilizes cluster resource computing nodes and improves the efficiency of data parallel application. This paper used mature Hadoop technology to build a large-scale distributed image management architecture for remote sensing imagery. Using MapReduce parallel processing technology, it called many computing nodes to process image storage blocks and pyramids in the background to improve the efficiency of image reading and application and sovled the need for concurrent multi-user high-speed access to remotely sensed data. It verified the rationality, reliability and superiority of the system design by testing the storage efficiency of different image data and multi-users and analyzing the distributed storage architecture to improve the application efficiency of remote sensing images through building an actual Hadoop service system.

Practical Secure Naïve Bayesian Classification Over Encrypted Big Data in Cloud

2017 ◽  
Vol 28 (06) ◽  
pp. 683-703 ◽  
Author(s):  
Youwen Zhu ◽  
Xingxin Li ◽  
Jian Wang ◽  
Yining Liu ◽  
Zhiguo Qu
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Large Scale ◽  
Cloud Service ◽  
Bayesian Classification ◽  
Training Data ◽  
Naive Bayesian ◽  
Naïve Bayesian ◽  
Security Proof ◽  
Data Owner

Cloud can provide much convenience for big data storage and analysis. To enjoy the advantage of cloud service with privacy preservation, huge data is increasingly outsourced to cloud in encrypted form. Unfortunately, encryption may impede the analysis and computation over the outsourced dataset. Naïve Bayesian classification is an effective algorithm to predict the class label of unlabeled samples. In this paper, we investigate naïve Bayesian classification on encrypted large-scale dataset in cloud, and propose a practical and secure scheme for the challenging problem. In our scheme, all the computation task of naïve Bayesian classification are completed by the cloud, which can dramatically reduce the burden of data owner and users. We give a formal security proof for our scheme. Based on the theoretical proof, we can strictly guarantee the privacy of both input dataset and output classification results, i.e., the cloud can learn nothing useful about the training data of data owner and the test samples of users throughout the computation. Additionally, we not only theoretically analyze our computation complexity and communication overheads, but also evaluate our implementation cost by leveraging extensive experiments over real dataset, which shows our scheme can achieve practical efficiency.

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