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Recognizing and removing of similar Data for Information Processing and Storing in Cloud

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  • Gundluru PadmajaKumari
  • C. Govardhan

Abstract

Attribute-based encryption (ABE) has been wide utilized in cloud computing wherever a data provider outsources his/her encrypted information to a cloud service provider, and might share the data with users possessing specific credentials (or attributes). However, the standard ABE system doesn't support secure deduplication, which is crucial for eliminating duplicate copies of identical data inorder to save lots of cupboard space and network information measure. During this paper, we have a tendency to gift DARE, a low-overhead Deduplication-Aware resemblance detection and Elimination theme that effectively exploits existing duplicate-adjacency data for extremely economical likeness detection in data deduplication based mostly backup/archiving storage systems. the most plan behind DARE is to use a theme, call Duplicate-Adjacency based mostly likeness Detection (DupAdj), by considering any 2 information chunks to be similar (i.e., candidates for delta compression) if their various adjacent information chunks are duplicate during a deduplication system, and so any enhance the resemblance detection efficiency by AN improved super-feature approach.

Suggested Citation

  • Gundluru PadmajaKumari & C. Govardhan, 2018. "Recognizing and removing of similar Data for Information Processing and Storing in Cloud," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 3(6), pages 527-534, July.
    • Handle: RePEc:jbh:ijsrcs:v3:y2018:i6:id:hcseit183652
    Note: Article URL: https://ijsrcseit.com/CSEIT183652
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