Mining the Statistical Information of Confidential Data from Noise-Multiplied Data
Yan‐Xia Lin · 2017
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.