scholarly journals SecHDFS: Efficient and Secure Data Storage Model over HDFS using RC6

2019 ◽  
Vol 9 (2) ◽  
pp. 3900-3903
Keyword(s):  
Data Storage ◽  
Research Work ◽  
Apache Hadoop ◽  
Storage Model ◽  
Proposed Model ◽  
Secure Data ◽  
Hadoop Distributed File System ◽  
Client Data ◽  
Symmetric Block ◽  
Big Data Storage

In today’s world the data used by various institutions and organizations is increasing and process petabytes of data per hour. Hence big data storage platform called Apache Hadoop is designed to process large amount of data, but it does not guarantee the security of user stored files in Hadoop. In this paper, a secure HDFS is designed for an efficient and secure data storage model. We encrypt and decrypt client data using RC6 symmetric block cipher. In this research work, Hadoop distributed file system (HDFS) is customized using RC6 which provides transparent end-to-end encryption on user’s data for read as well as write. Our proposed SecHDFS will mitigate several security attacks such as replay attacks, data node impersonating attacks, and brute-force attacks. The proposed model imparts better results than the inbuilt AES symmetric algorithm.

scholarly journals A New Secure Model for Data Protection over Cloud Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Amr M. Sauber ◽  
Passent M. El-Kafrawy ◽  
Amr F. Shawish ◽  
Mohamed A. Amin ◽  
Ismail M. Hagag
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Data Protection ◽  
Cloud Services ◽  
Security And Privacy ◽  
Storage Model ◽  
Cloud Data ◽  
Security Issues ◽  
Data Owner ◽  
Proposed Model

The main goal of any data storage model on the cloud is accessing data in an easy way without risking its security. A security consideration is a major aspect in any cloud data storage model to provide safety and efficiency. In this paper, we propose a secure data protection model over the cloud. The proposed model presents a solution to some security issues of cloud such as data protection from any violations and protection from a fake authorized identity user, which adversely affects the security of the cloud. This paper includes multiple issues and challenges with cloud computing that impairs security and privacy of data. It presents the threats and attacks that affect data residing in the cloud. Our proposed model provides the benefits and effectiveness of security in cloud computing such as enhancement of the encryption of data in the cloud. It provides security and scalability of data sharing for users on the cloud computing. Our model achieves the security functions over cloud computing such as identification and authentication, authorization, and encryption. Also, this model protects the system from any fake data owner who enters malicious information that may destroy the main goal of cloud services. We develop the one-time password (OTP) as a logging technique and uploading technique to protect users and data owners from any fake unauthorized access to the cloud. We implement our model using a simulation of the model called Next Generation Secure Cloud Server (NG-Cloud). These results increase the security protection techniques for end user and data owner from fake user and fake data owner in the cloud.

scholarly journals Big Data Storage System Based on a Distributed Hash Tables System

2020 ◽  
Vol 12 (5) ◽  
pp. 1-9
Author(s):  
Telesphore Tiendrebeogo ◽  
Mamadou Diarra
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Storage System ◽  
Research Work ◽  
Future Research ◽  
Distributed Hash Tables ◽  
Hash Tables ◽  
Wide Range ◽  
Data Storage System ◽  
Big Data Storage

The Big Data is unavoidable considering the place of the digital is the predominant form of communication in the daily life of the consumer. The control of its stakes and the quality its data must be a priority in order not to distort the strategies arising from their treatment in the aim to derive profit. In order to achieve this, a lot of research work has been carried out companies and several platforms created. MapReduce, is one of the enabling technologies, has proven to be applicable to a wide range of fields. However, despite its importance recent work has shown its limitations. And to remedy this, the Distributed Hash Tables (DHT) has been used. Thus, this document not only analyses the and MapReduce implementations and Top-Level Domain (TLD)s in general, but it also provides a description of a model of DHT as well as some guidelines for the planification of the future research.

scholarly journals A Survey on Accelerated Mapreduce for Hadoop

2017 ◽  
Vol 10 (3) ◽  
pp. 597-602
Author(s):  
Jyotindra Tiwari ◽  
Dr. Mahesh Pawar ◽  
Dr. Anjajana Pandey
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Energy Efficient ◽  
High Performance ◽  
Map Reduce ◽  
Efficient Computation ◽  
Apache Hadoop ◽  
Huge Data ◽  
Performance Techniques ◽  
Big Data Storage

Big Data is defined by 3Vs which stands for variety, volume and velocity. The volume of data is very huge, data exists in variety of file types and data grows very rapidly. Big data storage and processing has always been a big issue. Big data has become even more challenging to handle these days. To handle big data high performance techniques have been introduced. Several frameworks like Apache Hadoop has been introduced to process big data. Apache Hadoop provides map/reduce to process big data. But this map/reduce can be further accelerated. In this paper a survey has been performed for map/reduce acceleration and energy efficient computation in quick time.

“Saksham Model” Performance Improvisation Using Node Capability Evaluation in Apache Hadoop

2020 ◽  
pp. 206-230
Author(s):  
Ankit Shah ◽  
Mamta C. Padole
Keyword(s):  
Big Data ◽  
Distributed Computing ◽  
Data Processing ◽  
Data Storage ◽  
Model Performance ◽  
Big Data Processing ◽  
Apache Hadoop ◽  
Processing Capability ◽  
Proposed Model ◽  
Capability Evaluation

Big Data processing and analysis requires tremendous processing capability. Distributed computing brings many commodity systems under the common platform to answer the need for Big Data processing and analysis. Apache Hadoop is the most suitable set of tools for Big Data storage, processing, and analysis. But Hadoop found to be inefficient when it comes to heterogeneous set computers which have different processing capabilities. In this research, we propose the Saksham model which optimizes the processing time by efficient use of node processing capability and file management. The proposed model shows the performance improvement for Big Data processing. To achieve better performance, Saksham model uses two vital aspects of heterogeneous distributed computing: Effective block rearrangement policy and use of node processing capability. The results demonstrate that the proposed model successfully achieves better job execution time and improves data locality.

scholarly journals Experimental Characteristics Study of Data Storage Formats for Data Marts Development within Data Lakes

2021 ◽  
Vol 11 (18) ◽  
pp. 8651
Author(s):  
Vladimir Belov ◽  
Alexander N. Kosenkov ◽  
Evgeny Nikulchev
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Storage System ◽  
Apache Hadoop ◽  
Aggregated Data ◽  
Data Marts ◽  
Hadoop Platform ◽  
Analytical Platforms ◽  
Big Data Storage

One of the most popular methods for building analytical platforms involves the use of the concept of data lakes. A data lake is a storage system in which the data are presented in their original format, making it difficult to conduct analytics or present aggregated data. To solve this issue, data marts are used, representing environments of stored data of highly specialized information, focused on the requests of employees of a certain department, the vector of an organization’s work. This article presents a study of big data storage formats in the Apache Hadoop platform when used to build data marts.

scholarly journals Analysis and Experimental Study of HDFS Performance

TEM Journal ◽  
2021 ◽  
pp. 806-814
Author(s):  
Yordan Kalmukov ◽  
Milko Marinov ◽  
Tsvetelina Mladenova ◽  
Irena Valova
Keyword(s):  
Experimental Study ◽  
Big Data ◽  
Computer System ◽  
Data Storage ◽  
Fault Tolerant ◽  
Processing System ◽  
Daily Basis ◽  
Distributed File System ◽  
Hadoop Distributed File System ◽  
Big Data Storage

In the age of big data, the amount of data that people generate and use on a daily basis has far exceeded the storage and processing capabilities of a single computer system. That motivates the use of distributed big data storage and processing system such as Hadoop. It provides a reliable, horizontallyscalable, fault-tolerant and efficient service, based on the Hadoop Distributed File System (HDFS) and MapReduce. The purpose of this research is to experimentally determine whether (and to what extent) the network communication speed, the file replication factor, the files’ sizes and their number, and the location of the HDFS client influence the performance of the HDFS read/write operations.

scholarly journals Multi-Authority Secure Database for Enabling Authorized Encrypted Search with Privacy Preserving on Healthcare Databases

2020 ◽  
Vol 9 (1) ◽  
pp. 1671-1674
Keyword(s):  
Data Storage ◽  
Ad Hoc ◽  
Research Work ◽  
Data Access ◽  
Healthcare Sector ◽  
Distributed Data ◽  
Security Issues ◽  
Multiple Data ◽  
Access Scheme ◽  
Secure Data

Now a day’s in medical field number of application’s will develop for overcome the complexity of previous work. By using information technology and computer science provide various new techniques and medical equipment’s has improved digitalization in healthcare sector. In existing system much more advancement is providing to overcome the time and money of patients and perform exact treatments and store patient’s confidential records in securely but most important issues are security. To address the existing security issues to design and develop the proposed research work on security i.e. for patient’s confidential health data records in database servers. Existing work during data transmission can only protect the patient’s data records but they can’t stop the insider attacks. In proposed research work, first implement front end security with the help of keylogging technique, second to store patient’s confidential data in multiple data servers or chunks and to prevent the insider attacks and third and most important is access policy of search for encrypted data of multi-authority. The main contribution of this research work to assign patients data records in different chunks securely and applying the cryptosystems for security goals of a patient’s confidential records. Especially, proposed work advantages of SHA hashing technique to perform each and every user for access of particular data records. This research work explores secure data storage and sharing using proposed AES 128 encryption algorithm and Role Base Access Control (RBAC) for secure data access scheme for end user. This work also carried out backup server approach it works like proxy storage server for ad hoc data recovery for all distributed data servers.

Sign in / Sign up

Export Citation Format

Share Document