This paper is published in Volume-4, Issue-3, 2018
Area
Data Minning
Author
Shubhashree Sahoo
Co-authors
Gogu Swathi
Org/Univ
Teegala Krishna Reddy Engineering College, Hyderabad, Telangana, India
Pub. Date
14 May, 2018
Paper ID
V4I3-1386
Publisher
Keywords
Algorithm, k Nearest Neighbor distance, Privacy, Confidentiality, and Range Query.

Citationsacebook

IEEE
Shubhashree Sahoo, Gogu Swathi. Construction of private methodical query services in the cloud with RASP data commotion, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Shubhashree Sahoo, Gogu Swathi (2018). Construction of private methodical query services in the cloud with RASP data commotion. International Journal of Advance Research, Ideas and Innovations in Technology, 4(3) www.IJARIIT.com.

MLA
Shubhashree Sahoo, Gogu Swathi. "Construction of private methodical query services in the cloud with RASP data commotion." International Journal of Advance Research, Ideas and Innovations in Technology 4.3 (2018). www.IJARIIT.com.

Abstract

As digital technology is fast evolving and becoming an essential tool for businesses, the concept of cloud is evolved. The phenomenon of the cloud is described in terms of private and public. The proposed approach is based on the public cloud domain, which consists, numerous nodes with distributed computing resources in many different geographic locations. This approach leads the public cloud domain into several cloud partitions. The approach of distributed computing in the cloud simplifies the load balancing and allows database indexes to build over an encryption table. Many times, data into the cloud is stored by maintaining confidentiality, query privacy, efficient query processing at low cost (CPEL Criteria). However, the data owners always desire to submit their quires after realizing the privacy assurance of the cloud. In this aspect, researchers have introduced few techniques such as RASP (Random Space Perturbation), k-NN (k-Nearest Neighbor) Algorithm etc. The main problem across RASP technique is, generating the encryption key which is too large and its implementation makes the time and space overhead. The existing RASP data perturbation technique along with k-NN algorithm is exploited to furnish privacy to the cloud. Wherein, issues such as categorical data and leaked query in the model are identified and addressed, by holding no change in designing the k-NN-R algorithm.
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