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Machine Learning Privacy Aware Anonymization Using MapReduce Based Neural Network

U. Selvi*, S. Pushpa
Department of Computer Science and Engineering, St. Peter’s Institute of Higher Education and Research, Chennai, India
* Corresponding Author: U. Selvi. Email:

Intelligent Automation & Soft Computing 2022, 31(2), 1185-1196. https://doi.org/10.32604/iasc.2022.020164

Received 12 May 2021; Accepted 22 June 2021; Issue published 22 September 2021

Abstract

Due to the recent advancement in technologies, a huge amount of data is generated where individual private information needs to be preserved. A proper Anonymization algorithm with increased Data utility is required to protect individual privacy. However, preserving privacy of individuals whileprocessing huge amount of data is a challenging task, as the data contains certain sensitive information. Moreover, scalability issue in handling a large dataset is found in using existing framework. Many an Anonymization algorithm for Big Data have been developed and under research. We propose a method of applying Machine Learning techniques to protect and preserve the personal identities of Individuals in BigData framework, which is termed as BigData Privacy Aware Machine Learning. For addressing a large volume of data, MapReduce-based neural networks parallelism is taken into consideration with classification of data volume. Human contextual information as applied through collaborative Machine Learning is proposed. The result of our experiment shows that relating human knowledge to neural network and parallelism by MapReduce framework can yield a better and measurable classification results for large scale Applications.

Keywords

Privacy aware machine learning; anonymization; k-anonymity; bigdata; mapreduce; back-propagation neural network; machine learning

Cite This Article

U. Selvi and S. Pushpa, "Machine learning privacy aware anonymization using mapreduce based neural network," Intelligent Automation & Soft Computing, vol. 31, no.2, pp. 1185–1196, 2022.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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