Building Confidential & Efficient Query Services in the Cloud with RASP Perturbation
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Abstract
In this paper Cloud computing infrastructures are popularly used by peoples now a days. By using cloud users can save their cost for query services. But some of the data owners are hesitate to put their data’s in cloud because, sometimes the data may be hack when they use in cloud unless the confidentiality of data and secure query processing will be provided by the cloud provider.However, some data might be sensitive that the data owner does not want to move to the cloud unless the data confidentiality and query privacy are guaranteed. We propose the Random Space Encryption (RASP) approach that allows efficient range search with stronger attack resilience than existing efficiency-focused approaches. The random space perturbation (RASP) data perturbation method to provide secure and efficient range query and kNN query services for protected 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. It also preserves multidimensional ranges, which allows existing indexing techniques to be applied to speedup range query processing. The kNN-R algorithm is designed to work with the RASP range query algorithm to process the kNN queries.
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References
Xu, H., Guo, S., and Chen, K. ―Building confidential and efficient query services in the cloud with RASP data perturbation‖, IEEE Transactions on Knowledge and Data Engineering 26, 2 (2014).
K. Chen, R. Kavuluru, and S. Guo, ―RASP: Efficient Multidimensional Range Query on Attack-Resilient Encrypted Databases,‖ Proc. ACM Conf. Data and Application Security and Privacy, pp. 249-260, 2011.
R. Agrawal, J. Kiernan, R. Srikant, and Y. Xu, ―Order Preserving Encryption for Numeric Data,‖ Proc. ACM SIGMOD Int’l Conf. Management of Data (SIGMOD), 2004.
M. Armbrust, A. Fox, R. Griffith, A.D. Joseph, R.K. Andy Konwinski, G. Lee, D. Patterson, A. Rabkin, I. Stoica, and M. Zaharia, ―Above the Clouds: A Berkeley View of Cloud Computing,‖ technical report, Univ. of Berkerley, 2009.
J. Bau and J.C. Mitchell, ―Security Modeling and Analysis,‖ IEEE Security and Privacy, vol. 9, no. 3, pp. 18- 25, May/June 2011.
. M. L. Liu, G. Ghinita, C. S.Jensen, and P. Kalnis, ―Enabling search services on outsourced private spatial data,‖ The International Journal of on Very Large Data Base, vol. 19, no. 3, 2010.
. 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.
. M. Rudelson and R. Vershynin, ―Smallest singular value of a random rectangular matrix,‖ Communications on Pure and Applied Mathematics, vol. 62, pp. 1707–1739, 2009.
C. Wang, N. Cao, J. Li, K. Ren, and W. Lou, ―Secure ranked keyword search over encrypted cloud data,‖ in Proceedings of IEEE International Conference on Distributed Computing Systems (ICDCS), 2010.
. E. Shi, J. Bethencourt, T.-H. H. Chan, D. Song, and A. Perrig, ―Multi-dimensional range query over encrypted data,‖ in IEEE Symposium on Security and Privacy, 2007.
. P. Williams, R. Sion, and B. Carbunar, Building castles out of mud: Practical access pattern privacy and correctness on untrusted storage,‖ in ACM Conference on Computer and Communications Security, 2008.
. W. K. Wong, D. W.-l. Cheung, B. Kao, and N. Mamoulis, ―Secure knn computation on encrypted databases,‖ in Proceedings of ACM SIGMOD Conference. New York, NY, USA: ACM, 2009, pp. 139–152.