Enhanced Query Processing Technique Using RASP Effective Services in Cloud

N. Deshai¹ , P. Penchala Swamy² , G.P. Saradhi Varma³

1. Dept. Information Technology, S.R.K.R. Engineering, Bhimavaram, India.
2. Dept. Information Technology, S.R.K.R. Engineering, Bhimavaram, India.
3. Dept. Computer Science and Engineering, S.R.K.R. Engineering, Bhimavaram, India.

Correspondence should be addressed to: desaiphd7@gmail.com.

Section:Research Paper, Product Type: Isroset-Journal
Vol.5 , Issue.4 , pp.76-78, Aug-2017

Online published on Aug 30, 2017

Copyright © N. Deshai, P. Penchala Swamy, G.P. Saradhi Varma . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Cloud computing is an emerging technology, Privacy and confidentiality has become the major concern in the public cloud. Data owners do not want to move their data to the cloud until and unless the confidentiality and the query privacy are preserved. On the other hand a secured query services should provide efficient query processing and reduce the in-house workload to get the total benefit of cloud computing. This paper presents a Random space perturbation method to provide secure and efficient range query and K nearest neighbor query services for protecting data in the cloud. The RASP data perturbation method combines order preserving encryption, dimensionality expansion, random noise injection, and random projection, to provide strong resilience to attacks on the perturbed data and queries.The kNN-R algorithm is designed to work with the RASP range query algorithm to process the kNN queries.

Key-Words / Index Term :
Privacy, Confidentiality, Range, query, KNN Privacy

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