A Review on Secure Data Deduplication: Cloud Storage Security Issue

Prajapati, Priteshkumar; Shah, Parth

doi:10.1016/j.jksuci.2020.10.021

Cited by 55 publications

(21 citation statements)

References 19 publications

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“…Loss compression removes information by defining and irretrievably eliminating redundant information as is typical of JPEG compression techniques. This article concentrates on the lossless type of compression, particularly data deduplication as lossless compression for general storage systems is needed [12].…”

Section: Reducing Data Redundancymentioning

confidence: 99%

Decentralized and Privacy Sensitive Data De-Duplication Framework for Convenient Big Data Management in Cloud Backup Systems

Jeslin

Kumar²

2022

Symmetry

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The number of customers transferring information to cloud storage has grown significantly, with the rising prevalence of cloud computing. The rapidly rising data volume in the cloud, mostly on one side, is followed by a large replication of data. On the other hand, if there is a single duplicate copy of stored symmetrical information in the de-duplicate cloud backup the manipulation or lack of a single copy may cause untold failure. Thus, the deduplication of files and the auditing of credibility are extremely necessary and how they are achieved safely and effectively must be addressed in academic and commercial contexts urgently. In order to tune in this task by using application recognition, data similitude, and locality to simplify decentralized deduplication with two-tier internode and application deduction, we suggest a flexible direct decentralized symmetry deduplication architecture in a cloud scenario. It first distributes application logic to the contents of the directory through implementation-oriented steering to maintain a deployment location and also attributes the same kind of information to the cloud backup node with the storage node specificity by means of a hand printing-based network model to attain adequate global deduplication performance. We build up a new ownership mechanism during file deduplication to ensure continuity of tagging and symmetrical modeling and verify shared ownership. In addition, we plan an effective ownership policy maintenance plan. In order to introduce a probabilistic key process and reduce key storage capacity, a user-helped key is used for in-user block deduplication. Finally, the protection and efficiency audit demonstrate that the data integrity and accuracy of our system are ensured and symmetrically effective in the management of data ownership.

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Section: Reducing Data Redundancymentioning

confidence: 99%

Decentralized and Privacy Sensitive Data De-Duplication Framework for Convenient Big Data Management in Cloud Backup Systems

Jeslin

Kumar²

2022

Symmetry

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“…The tremendous growth in the usage of mobile and web applications leads to exponential increase in the data across the globe. Hence, storage space is essential for storing the data has become the most important concern [8]. Provisioning of storage optimization techniques became a vital constraint to huge storage capacities like cloud storage.…”

Section: Jiang Et Al [18]mentioning

confidence: 99%

Computing Time Series Data During Index Based De-Duplication of Industrial IoT Data in Cloud Environment

Muthunagai¹,

Anitha²

2021

Preprint

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As a result of the development in Industry 4.0, the data generated within the Industries are increasing rapidly every day to attain the innovative environment within the industry through maximal asset utilization. Meanwhile, the redundancy rate in the server is also increasing, which has an impact on the storage as well as in the analysis of data. Most existing de-duplication techniques partition the data with respect to memory. However if the time period is considered for partition, time-series analysis would be achieved during the de-duplication process. To address the above issue, the proposed work presents the Index Based De-duplication technique with Categorized Region Method for computing time-series data. The Merkle Tree with a super feature called reckoning of occurrence is combined in the proposed system to rapidly identify the existence of similar data in the distributed system with an accurate existence count that significantly helps in predicting the future drifts of the industrial environment. Finally, the proposed system also concludes with optimal transportation cost to reach the storage nodes in the cloud using MODI method. The experimental results reveal that the proposed model is efficient since it facilitates less memory and less computation overhead. The proposed technique achieves space reduction by 98%, reduces the computation overhead during analysis by 55%, and increases the efficacy of cloud storage by 60%.

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“…In 2020, Prajapati and Shah [24] discussed regarding the efficient data storage and cloud service providers.…”

Section: Related Workmentioning

confidence: 99%

An analytical review and analysis for the data control and security in cloud computing

Samvatsar¹,

Kanungo²

2020

IJATEE

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In this paper an analytical review and analysis for the data control and security in cloud computing was presented and analysed. This review and analysis covers three aspects. First is the study of the wide acceptability of cloud computing and its areas of applicability. Second is the security aspect in terms of data handling and data sharing. Third aspect is the interdisciplinary areas like Internet of Things (IoT) and big data in terms of collaboration with cloud computing environment. These components were discussed and analysed with the parametric evaluation along with the analytical variations. This paper presented an analytical view with the exploration of authenticity and data integrity. It also shows the result analysis with the performance measures. It also highlights the gaps along with the solution methodology with insightful discussion. This paper also highlights the problem statements with the suggested solutions and the future work in the same direction.

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A Review on Secure Data Deduplication: Cloud Storage Security Issue

Cited by 55 publications

References 19 publications

Decentralized and Privacy Sensitive Data De-Duplication Framework for Convenient Big Data Management in Cloud Backup Systems

Decentralized and Privacy Sensitive Data De-Duplication Framework for Convenient Big Data Management in Cloud Backup Systems

Computing Time Series Data During Index Based De-Duplication of Industrial IoT Data in Cloud Environment

An analytical review and analysis for the data control and security in cloud computing

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