Title: A computational Bayesian approach for estimating density functions based on noise-multiplied data

Authors: Yan-Xia Lin

Addresses: National Institute for Applied Statistics Research Australia, School of Mathematics and Applied Statistics, University of Wollongong, Wollongong, Australia

Abstract: In this big data era, an enormous amount of personal and company information can be easily collected by third parties. Sharing the data with the public and allowing data users to access the data for data mining often bring many benefits to the public. However, sharing the microdata with the public usually causes the issue of data privacy. Protecting data privacy through noise-multiplied data is one of the approaches studied in the literature. This paper introduces the B-M L2014 Approach for estimating the density function of the original data based on noise-multiplied microdata. This paper shows applications of the B-M L2014 Approach and demonstrates that the statistical information of the original data can be retrieved from their noise-multiplied data reasonably while the disclosure risk is under control. The B-M L2014 Approach provides a new data mining technique for big data when data privacy is concerned.

Keywords: big data mining; data anonymisation; privacy-preserving; microdata confidentiality; noise-multiplied data.

DOI: 10.1504/IJBDI.2019.100880

International Journal of Big Data Intelligence, 2019 Vol.6 No.3/4, pp.143 - 152

Received: 23 Dec 2017
Accepted: 16 May 2018

Published online: 04 Jun 2019 *

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