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Secure Deduplication Scheme for Cloud Encrypted Data

Secure Deduplication Scheme for Cloud Encrypted Data

Vishal Passricha, Ashish Chopra, Shubhanshi Singhal
Copyright: © 2019 |Volume: 11 |Issue: 2 |Pages: 14
ISSN: 1937-965X|EISSN: 1937-9668|EISBN13: 9781522564959|DOI: 10.4018/IJAPUC.2019040103
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MLA

Passricha, Vishal, et al. "Secure Deduplication Scheme for Cloud Encrypted Data." IJAPUC vol.11, no.2 2019: pp.27-40. http://doi.org/10.4018/IJAPUC.2019040103

APA

Passricha, V., Chopra, A., & Singhal, S. (2019). Secure Deduplication Scheme for Cloud Encrypted Data. International Journal of Advanced Pervasive and Ubiquitous Computing (IJAPUC), 11(2), 27-40. http://doi.org/10.4018/IJAPUC.2019040103

Chicago

Passricha, Vishal, Ashish Chopra, and Shubhanshi Singhal. "Secure Deduplication Scheme for Cloud Encrypted Data," International Journal of Advanced Pervasive and Ubiquitous Computing (IJAPUC) 11, no.2: 27-40. http://doi.org/10.4018/IJAPUC.2019040103

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Abstract

Cloud storage (CS) is gaining much popularity nowadays because it offers low-cost and convenient network storage services. In this big data era, the explosive growth in digital data moves the users towards CS but this causes a lot of storage pressure on CS systems because a large volume of this data is redundant. Data deduplication is an effective data reduction technique. The dynamic nature of data makes security and ownership of data as a very important issue. Proof-of-ownership schemes are a robust way to check the ownership claimed by any owner. However, this method affects the deduplication process because encryption methods have varying characteristics. A convergent encryption (CE) scheme is widely used for secure data deduplication. The problem with the CE-based scheme is that the user can decrypt the cloud data while he has lost his ownership. This article addresses the problem of ownership revocation by proposing a secure deduplication scheme for encrypted data. The proposed scheme enhances the security against unauthorized encryption and poison attack on the predicted set of data.

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