Research on Data Destruction Mechanism with Security Level in HDFS

2013 ◽  
Vol 834-836 ◽  
pp. 1795-1798
Author(s):  
Jun Qin ◽  
Ya Ping Zhang ◽  
Ping Zong
Keyword(s):  
Data Security ◽  
Cloud Storage ◽  
Storage System ◽  
Security Requirements ◽  
Security Level ◽  
Primary Concern ◽  
Simulation Experiments ◽  
Performance Requirements ◽  
Destruction Mechanism ◽  
And Performance

In cloud computing applications, the data security is a primary concern of user. In this paper, for the problem that data of the HDFS cannot be destroyed completely in open source cloud storage system, which may lead to data leakage, it designs a destruction mechanism of HDFS with multiple security level. This mechanism make data effectively destroyed by the method of data overwrite which makes a balance between security requirements and performance requirements. The Simulation experiments show that the mechanism can override a Block file in HDFS environment effectively to achieve the purpose of data destroying. At the same the overhead of different overwrite algorithm is different also which can ensure the security and efficiency is balanced.

scholarly journals Multi-security-level cloud storage system based on improved proxy re-encryption

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Jinan Shen ◽  
Xuejian Deng ◽  
Zhenwu Xu
Keyword(s):  
Performance Optimization ◽  
Cloud Storage ◽  
Storage System ◽  
Security Requirements ◽  
Control Factor ◽  
Security Level ◽  
Fine Grained ◽  
Attribute Based Encryption ◽  
And Performance ◽  
Heterogeneous Cloud

AbstractBased on the characteristics and data security requirements of the cloud environment, we present a scheme for a multi-security-level cloud storage system that is combined with AES symmetric encryption and an improved identity-based proxy re-encryption (PRE) algorithm. Our optimization includes support for fine-grained control and performance optimization. Through a combination of attribute-based encryption methods, we add a fine-grained control factor to our algorithm in which each authorization operation is only valid for a single factor. By reducing the number of bilinear mappings, which are the most time-consuming processes, we achieve our aim of optimizing performance. Last but not least, we implement secure data sharing among heterogeneous cloud systems. As shown in experiment, our proposed multi-security-level cloud storage system implements services such as the direct storage of data, transparent AES encryption, PRE protection that supports fine-grained and ciphertext heterogeneous transformation, and other functions such as authentication and data management. In terms of performance, we achieve time-cost reductions of 29.8% for the entire process, 48.3% for delegation and 47.2% for decryption.

scholarly journals OB-MECC: An Efficient Confidentiality and Security Enhancement for Cloud Storage System

2021 ◽  
Author(s):  
P. NagaRaju ◽  
N. Nagamalleswara Rao
Keyword(s):  
Cloud Computing ◽  
Data Security ◽  
Cloud Storage ◽  
Storage System ◽  
Cloud Services ◽  
Security Level ◽  
Data Confidentiality ◽  
Security Enhancement ◽  
Reducing Costs ◽  
Main Barrier

Cloud computing (CC) is one amongst the developing technologies, which get more attention from academia as well as industries. It offers diverse benefits like sharing computing resources, service flexibility, reducing costs, etc. The Cloud Services Provider (CSP) is accountable for the data that are delivered to the cloud. The threat of seeing the stored data and using sensitive raw data by strangers is the main barrier in the utilization of cloud services. So, Data Security (DS) along with privacy is the chief issue, which is an obstacle while adopting the CC. Countless techniques are existent for ensuring data confidentiality, but they do not completely give protection to the data. To trounce these drawbacks, this paper introduces the Obfuscation (OB) based Modified Elliptical Curve Cryptography (MECC) algorithm for protecting data as of malicious attacks, which is termed as OB-MECC. Primarily, the proposed method obfuscates the data before they are uploaded to the cloud. For the OB of the data, the proposed work employs methods like substitution cipher (SC), position update, Ceaser cipher, binary conversion, 8-bit binary conversion, decimal(),  two complex(), and ASCII(). Then, encryption of the obfuscated data is done with the utilization of the MECC algorithm. After encryption, the data on the cloud is retrieved. The retrieved data is then decrypted by reversing the OB and encryption process to get the actual data. The outcomes corroborate that the confidentiality and security level are maximum for the proposed OB-MECC when contrasted to the existing approaches.

Study on Cloud Data Destruction Mechanism and its Application

2013 ◽  
Vol 477-478 ◽  
pp. 1487-1490
Author(s):  
Jing Wu ◽  
Feng Zhi Zhao ◽  
Yu Dan Dong
Keyword(s):  
Data Security ◽  
Service Providers ◽  
Cloud Service ◽  
Primary Concern ◽  
Cloud Data ◽  
Safety Requirements ◽  
Destruction Mechanism ◽  
Cloud Environments ◽  
Multi Level ◽  
And Performance

The cloud data security is the primary concern users, especially in multi-tenant cloud environments residual data can cause data leakage problem, but most of the cloud service providers do not provide data processing residual solution. This study was designed HDFS multi-level security mechanisms and data destruction and data based on bidirectional heartbeat overwrite technology combined with the data from the destruction mechanism to ensure effective destruction of data under the premise of safety requirements and performance can be achieved demand balance.

A Study on Cloud Storage Security Method Using Data Classification

2021 ◽  
Vol 23 (09) ◽  
pp. 1105-1121
Author(s):  
Dr. Ashish Kumar Tamrakar ◽  
◽  
Dr. Abhishek Verma ◽  
Dr. Vishnu Kumar Mishra ◽  
Dr. Megha Mishra ◽  
...  
Keyword(s):  
Cloud Storage ◽  
Storage Systems ◽  
Data Classification ◽  
Data Encryption ◽  
Security Requirements ◽  
Security And Privacy ◽  
Security Level ◽  
Distributed Resources ◽  
Storage Security ◽  
Public Data

Cloud computing is a new model for providing diverse services of software and hardware. This paradigm refers to a model for enabling on-demand network access to a shared pool of configurable computing resources, that can be rapidly provisioned and released with minimal service provider interaction .It helps the organizations and individuals deploy IT resources at a reduced total cost. However, the new approaches introduced by the clouds, related to computation outsourcing, distributed resources and multi-tenancy concept, increase the security and privacy concerns and challenges. It allows users to store their data remotely and then access to them at any time from any place .Cloud storage services are used to store data in ways that are considered cost saving and easy to use. In cloud storage, data are stored on remote servers that are not physically known by the consumer. Thus, users fear from uploading their private and confidential files to cloud storage due to security concerns. The usual solution to secure data is data encryption, which makes cloud users more satisfied when using cloud storage to store their data. Motivated by the above facts; we have proposed a solution to undertake the problem of cloud storage security. In cloud storage, there are public data that do not need any security measures, and there are sensitive data that need applying security mechanisms to keep them safe. In that context, data classification appears as the solution to this problem. The classification of data into classes, with different security requirements for each class is the best way to avoid under security and over security situation. The existing cloud storage systems use the same Journal of University of Shanghai for Science and Technology ISSN: 1007-6735 Volume 23, Issue 9, September – 2021 Page-1105 key size to encrypt all data without taking into consideration its confidentiality level. Treating the low and high confidential data with the same way and at the same security level will add unnecessary overhead and increase the processing time. In our proposal, we have combined the K-NN (K Nearest Neighbors) machine learning method and the goal programming decision-making method, to provide an efficient method for data classification. This method allows data classification according to the data owner security needs. Then, we introduce the user data to the suitable security mechanisms for each class. The use of our solution in cloud storage systems makes the data security process more flexible, besides; it increases the cloud storage system performance and decreases the needed resources, which are used to store the data.

A Block Chain Approach for Privacy Preserving and Data Security in Internet of Things

2020 ◽  
Vol 17 (9) ◽  
pp. 3979-3982
Author(s):  
N. Harish Kumar ◽  
G. Deepak
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Data Security ◽  
Cloud Storage ◽  
Storage System ◽  
Information Storage ◽  
Entire System ◽  
Time Data ◽  
Real Time Data ◽  
Block Chain

Internet of Things has been increasing its usage and recognition in vast sectors like Defence, Business, Industries, and Hospitals. The data disruption is strictly unacceptable in a number of these sectors because it could end up in serious Loss or Damages to the entire system. As of now, IOT is using a central cloud storage system for information storage and transactions. However, some examples already verified that Central cloud storage information might be hacked and changed by the specialists. This paper presents an IoT system having localized block chain storage which works on real time data and manipulates with narrowness of data interruption and modification and its recovery.

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