Karnati Rajesh, T.V. Ramanamma


Big information in a number of cloud programs will enhance very consistent with big data trend, thus that makes it challenge for generally used tools to deal with, important data in the reasonable passed time.  Hence is a crucial issue existing techniques to attain repair off privacy on privacy-sensitive important data sets because of the lack of scalability. The research efforts have started to check out scalability impracticality of extensive data anonymization. Data sets were so huge that anonymizing of individuals data sets has switched in to a challenge for conventional computations. Inside our work we leverage Map Reduce, that's a parallel human resources structure, to tackle scalability impracticality of top-lower specialization way of important data anonymization. Inside our work we commence a really efficient two-phase top-lower specialization method for data anonymization that is founded on Map Reduce above cloud system. In phases within our system, we intend cluster of pioneering Map Reduce jobs to achieve specialization computation in very method. The forecasted plan's transported to handle computation necessary in top-lower specialization approach inside an extremely powerful approach.



Cloud Applications; Datasets; Big Data; Data Anonymization; Map Reduce; Two-Phase Top-Down Specialization; Data Processing;


N. Mohammed, B.C. Fung, and M. Debbabi, “Anonymity Meets Game Theory: Secure Data Integration with Malicious Participants,” VLDB J., vol. 20, no. 4, pp. 567-588, 2011.

B. Fung, K. Wang, L. Wang, and P.C.K. Hung, “Privacy- Preserving Data Publishing for Cluster Analysis,” Data and Knowledge Eng., vol. 68, no. 6, pp. 552-575, 2009.

H. Takabi, J.B.D. Joshi, and G. Ahn, “Security and Privacy Challenges in Cloud Computing Environments,” IEEE Security and Privacy, vol. 8, no. 6, pp. 24-31, Nov. 2010.

D. Zissis and D. Lekkas, “Addressing Cloud Computing Security Issues,” Future Generation Computer Systems, vol. 28, no. 3, pp. 583- 592, 2011.

X. Zhang, C. Liu, S. Nepal, S. Pandey, and J. Chen, “A Privacy Leakage Upper-Bound Constraint Based Approach for Cost- Effective Privacy Preserving of Intermediate Data Sets in Cloud,” IEEE Trans. Parallel and Distributed Systems, to be published, 2012.

L. Sweeney, “k-Anonymity: A Model for Protecting Privacy,” Int’l J. Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 10, no. 5, pp. 557-570, 2002.

Full Text: PDF


  • There are currently no refbacks.

Copyright © 2012 - 2020, All rights reserved.| ijitr.com

Creative Commons License
International Journal of Innovative Technology and Research is licensed under a Creative Commons Attribution 3.0 Unported License.Based on a work at IJITR , Permissions beyond the scope of this license may be available at http://creativecommons.org/licenses/by/3.0/deed.en_GB.