Achieving Efficient and Privacy-Preserving Cross-Domain Big Data Deduplication in Cloud - 2017

PROJECT TITLE :

Achieving Efficient and Privacy-Preserving Cross-Domain Big Data Deduplication in Cloud - 2017

ABSTRACT:

Secure data deduplication will considerably reduce the Communication and storage overheads in cloud storage services, and has potential applications in our huge information-driven society. Existing information deduplication schemes are generally designed to either resist brute-force attacks or ensure the potency and information availability, however not both conditions. We tend to are also not awake to any existing theme that achieves accountability, within the sense of reducing duplicate info disclosure (e.g., to work out whether plaintexts of two encrypted messages are identical). In this paper, we tend to investigate a three-tier cross-domain design, and propose an efficient and privacy-preserving massive information deduplication in cloud storage (hereafter called EPCDD). EPCDD achieves both privacy-preserving and information availability, and resists brute-force attacks. Still, we have a tendency to take accountability into consideration to offer better privacy assurances than existing schemes. We have a tendency to then demonstrate that EPCDD outperforms existing competing schemes, in terms of computation, Communication and storage overheads. In addition, the time complexity of duplicate search in EPCDD is logarithmic.

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