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Published
Apr 25, 2015
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Abstract

Cloud computing has significant efficiency and cost advantages, here is an appealing solution for the cost efficient clouds especially for the pay as you go model.  We propose a scheme called Advanced Aggregation & Distribution of query services (AADQ’s). In this paper, we address Privacy and Efficiency are the two fundamental issues. Without heavy querying this scheme allows users to retrieve files of their interest without leaking any information which is subject to privacy. It also aggregates user’s queries in a short time delay, based on multi keyword and multi ranking techniques, which retrieves higher percentage of matched files. The AADQ contains a caching mechanism which overcomes the drawback of sending redundant queries to the cloud, which intern saves the cost. As it provides an efficient solution to reduce querying overhead incurred on the cloud. This feature is useful when cloud retrieves large numbers of matched files, but the user needs only a few of them. Extensive evaluations have been conducted to show the advantages of this approach.

Keywords: Multikeyword, Multiranking, Query Services, Caching Mechanism.

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Author Biographies

Pavan Kumar J, Shridevi Institute of Engineering and Technology, Tumkur

Master of Technology, Department of Computer Science

Shanmukha Swamy C.V, Shridevi Institute of Engineering and Technology, Tumkur.

Assistant Professor, Department of Compuer science

Priyardarshini M.M, Shridevi Institute of Engineering and Technology, Tumkur.

Master of Technology, Department of Computer Science
How to Cite
J, P. K., C.V, S. S., & M.M, P. (2015). AADQ’s: Advances in Aggregation & Distribution of Query services. International Journal of Emerging Trends in Science and Technology, 2(04). Retrieved from https://ijetst.igmpublication.org/index.php/ijetst/article/view/650

References

1. Qin Liu, Chiu C, Jibe Wu, and Guojun Wang, (2014) Towards Differential Query services in a Cost-Efficient Clouds, ‟IEEE Transactions on parallel and Distributed Systems R.J.SOLOMONOFF,Member,IEEE “Some recent works in Artificial Intelligence,” Proceedings of the IEEE,VOL. 54,NO. 12,December 1966.
2. Boone. D, Crescenzo. D, Ostrovsky. R, and Persiano. G, (2004) Public- Key with Keyword Search, ‟ Proc. Int‟l Conf. Theory and Applications of Cryptographic Techniques
3. Cao.N,Wang.C, Ren.M Li, K. And Lou. W, (2011) „Privacy-Preserving Multi keyword Ranked Search over Encrypted Cloud Data, ‟ Proc. IEEEINFOCOM..
4. Coron. J. S, Mandal. A, Naccache. D and Tibouchi. M, (2011) „Fully Homomorphic Encryption over the Integers with ShorterPublic Keys, ‟ CRYPTO‟11: Proc. 31st Ann. Conf. Advances in Cryptology Jong-Myoung Kim, Seon-Ho Park, Young-Ju Han and Tai-Myoung Chung, “CHEF: Cluster Head Election mechanism using Fuzzy logic in Wireless Sensor Networks,” ISBN 978-89-5519-136-3,Feb. 17-20, 2008 ICACT 2008.
5. Curtmola. R, Garay. J. A, Kamara. S, and Ostrovsky. R, (2006) „Searchable Symmetric Encryption: Improved Definitions and Efficient Constructions, ‟Proc. ACM 13th Conf. Computer and Comm. Security Junpei Anno,Leonard Barolli, “A Cluster Head
6. Gogle. P, Staddon. J, and Waters. B, (2004) ‟SecureConjunctive Keyword Search over Encrypted Data, ‟ Proc. Second Int‟l Conf. Applied Cryptography and Network Security (ACNS), pp. 31-45.
7. Hu.H, Xu.J, Ren.C, and Choi.B, (2011) „Processing Private Queries over Untrusted Data Cloud through Privacy Homomorphism,‟ Proc. IEEE 27th Int‟l Conf. Data Eng. (ICDE).