Privacy Preserving Verification Scheme for Cloud Platform Using DML

Abstract Deduplication technology is familiar in cloud-based services. It is used to reduce the space and the bandwidth requirements as a result, it reduces the redundancy and stores only the original copy. Deduplication finds its usage when a group of users stores the same data to the cloud storage...

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Bibliographic Details
Published in:Journal of Physics: Conference Series
Main Authors: Sujaritha, Akshara, D, Ashfak Ahamed, A, Chandhini shri, V S
Format: Article in Journal/Newspaper
Language:unknown
Published: IOP Publishing 2021
Subjects:
DML
Online Access:http://dx.doi.org/10.1088/1742-6596/1916/1/012154
https://iopscience.iop.org/article/10.1088/1742-6596/1916/1/012154
https://iopscience.iop.org/article/10.1088/1742-6596/1916/1/012154/pdf
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Summary:Abstract Deduplication technology is familiar in cloud-based services. It is used to reduce the space and the bandwidth requirements as a result, it reduces the redundancy and stores only the original copy. Deduplication finds its usage when a group of users stores the same data to the cloud storage service, but by using this method there is an issue regarding the ownership and security, and before getting into this process many users first encrypt their data and then share it with the cloud storage so that there is minimum or no privacy issue found. Recently so many models were released to solve this problem. That was actually done by sharing the same encrypted data for the same data which is shared by each owner. But by following this described method many flaws were found. We present a novel server-side deduplication model for all encrypted data in this paper. By following this model there is a proper control over the delegated data even after the control changes from person to person. So, Random convergent encryption and stable ownership key distribution are used. Through this method, the data theft can be prevented not only to the reward user but also to the old user who previously owned that data.