SECURE CONTENT DE-DUPLICATION UTILIZING EFFICIENT CONTENT DISCOVERY AND PRESERVING DE-DUPLICATION (ECDPD

ICTACT Journal on Data Science and Machine Learning ( Volume: 1 , Issue: 1 )

Abstract

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Cloud computing is a promising technology which is utilizing huge amount of data file storage with security. However, the content owner does not control data access for unauthorized clients and does not control data storage and usage of data. Some previous approaches data access control to help data de-duplication concurrently for cloud storage system. Current industrial de-duplication solutions can’t handle encrypted data for cloud storage. The deduplication is vulnerable to brute-force attacks and can’t flexibly support data access control. Data de-duplication is one of important data compression techniques for eliminating duplicate copies of repeating data, and has been widely used in cloud storage to reduce the amount of storage space and save bandwidth. To overcome above issues, an efficient content discovery and preserving De-duplication (ECDPD) algorithm is proposed for detecting client file level and block level of de-duplication in storing data files in the cloud storage system and support secure data access control at the similar time. The proposed system is protecting the confidentiality of sensitive data while supporting deduplication before data outsourcing. The system protects data security and attempt to formally address the problem of authorized data de-duplication. Based on Experimental evaluations, proposed ECDPD method reduces 3.802 milliseconds of DUT (Data Uploading Time) and 3.318 milliseconds of DDT (Data Downloading Time) compared than existing approaches.

Authors

A Mathew Branesh, S Johnson, F Antony Xavier Bronson
Dr. M.G.R. Educational and Research Institute, India

Keywords

Efficient Content Discovery and Preserving De-Duplication (ECDPD), Cloud Storage System, Data De-Duplication, Data Uploading Time, Data Downloading Time

Published By
ICTACT
Published In
ICTACT Journal on Data Science and Machine Learning
( Volume: 1 , Issue: 1 )
Date of Publication
December 2019
Pages
17-21
DOI

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