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Ameliorating the Privacy on Large Scale Aviation Dataset by Implementing MapReduce Multidimensional Hybrid k-Anonymization

Ameliorating the Privacy on Large Scale Aviation Dataset by Implementing MapReduce Multidimensional Hybrid k-Anonymization

Stephen Dass A., Prabhu J.
Copyright: © 2019 |Volume: 11 |Issue: 2 |Pages: 27
ISSN: 1938-0194|EISSN: 1938-0208|EISBN13: 9781522565192|DOI: 10.4018/IJWP.2019070102
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MLA

Stephen Dass A., and Prabhu J. "Ameliorating the Privacy on Large Scale Aviation Dataset by Implementing MapReduce Multidimensional Hybrid k-Anonymization." IJWP vol.11, no.2 2019: pp.14-40. http://doi.org/10.4018/IJWP.2019070102

APA

Stephen Dass A. & Prabhu J. (2019). Ameliorating the Privacy on Large Scale Aviation Dataset by Implementing MapReduce Multidimensional Hybrid k-Anonymization. International Journal of Web Portals (IJWP), 11(2), 14-40. http://doi.org/10.4018/IJWP.2019070102

Chicago

Stephen Dass A., and Prabhu J. "Ameliorating the Privacy on Large Scale Aviation Dataset by Implementing MapReduce Multidimensional Hybrid k-Anonymization," International Journal of Web Portals (IJWP) 11, no.2: 14-40. http://doi.org/10.4018/IJWP.2019070102

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Abstract

In this fast growing data universe, data generation and data storage are moving into the next-generation process by generating petabytes and gigabytes in an hour. This leads to data accumulation where privacy and preservation are certainly misplaced. This data contains some sensitive and high privacy data which is to be hidden or removed using hashing or anonymization algorithms. In this article, the authors propose a hybrid k anonymity algorithm to handle large scale aircraft datasets with combined concepts of Big Data analytics and privacy preservation of storing the dataset with the help of MapReduce. This published anonymized data are moved by MapReduce to the Hive database for data storage. The authors propose a multi-dimensional hybrid k-anonymity technique to solve the privacy issue and compare the proposed system with other two anonymization methods such as BUG and TDS. Three experiments were performed for evaluating classifier error, calculating disruption value and p% hybrid anonymity and estimation of processing time.

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