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.
 

View this paper at   Google Scholar | DPI Digital Library

XML View     PDF Download

How to Cite this Paper

258 Views    285 Downloads    242 Downloads

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

References :
[1] N. Cao, C. Wang, M. Li, K. Ren, and W. Lou, “Privacy preserving multi-keyword ranked search over encrypted cloud data,” in INFOCOMM, 2011.
[2] Huiqi Xu, Shumin Geo, Keke Chen, ”Building confidential and Efficient Query services in the Cloud with RASP Data Parturbation” IEEE Transaction on knowledge and data Engineering vol:26 no:2,2014.
[3] M. Armbrust, A. Fox, R. Griffith, A. D. Joseph, R. K. andAndy Konwinski, G. Lee, D. Patterson, A. Rabkin, I. Stoica, and M. Zaharia, “Above the clouds: A berkeley view of cloud computing,” Technical Report, University of Berkerley, 2009.
[4] H. Hacigumus, B. Iyer, C. Li, and S. Mehrotra, “Executing sql over encrypted data in the database-service-provider model,” in Proceedings of ACM SIGMOD Conference, 2002.
[5] R. Agrawal, J. Kiernan, R. Srikant, and Y. Xu, “Order preserving encryption for numeric data,” in Proceedings of ACM SIGMOD Conference, 2004.
[6] B. Chor, E. Kushilevitz, O. Goldreich, and M. Sudan, “Private information retrieval,” ACM Computer Survey, vol. 45, no. 6, pp. 965–981, 1998.
[7] B. Hore, S. Mehrotra, and G. Tsudik, “A privacy-preserving index for range queries,” in Proceedings of Very Large Databases Conference (VLDB), 2004.
[8] J. Bau and J. C. Mitchell, “Security modeling and analysis,” IEEE Security and Privacy, vol. 9, no. 3, pp. 18–25, 2011.
[9] K. Chen, L. Liu, and G. Sun, “Towards attack-resilient geometric data perturbation,” in SIAM Data Mining Conference, 2007.
[10] M. F. Mokbel, C. yin Chow, and W. G. Aref, “The new casper: Query processing for location services without Compromising privacy,” in Proceedings of Very Large Databases Conference (VLDB), 2006, pp. 763–774.
[11] B. Chor, E. Kushilevitz, O. Goldreich, and M. Sudan, “Private information retrieval,” ACM Computer Survey, vol. 45, no. 6, pp. 965–981, 1998.
[12] H. Hacigumus, B. Iyer, C. Li, and S. Mehrotra, “Executing sql over encrypted data in the database-service-provider model,” in Proceedings of ACM SIGMOD Conference, 2002.