Mining the statistical information of confidential data from noise-multiplied data

Authors

Yan-Xia Lin, University of WollongongFollow

RIS ID

127235

Publication Details

Lin, Y. (2017). Mining the statistical information of confidential data from noise-multiplied data. 2017 IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence and Computing, 3rd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (pp. 1292-1297). United States: IEEE Computer Society.

Abstract

Protecting data privacy and mining statistical information from protected data are the essential issues in big data. Protecting data privacy through noise-multiplied data is one of approaches studied in the literature. This paper introduces the B-M L2014 Approach for estimating the density function of the original data based on micro noise-multiplied data.We show an application 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. The approach provides a new data mining technique for big data when data privacy is concerned.