Estimation of Regression Parameters from Noise Multiplied Data - Yan-Xia Lin; Phillip Wise (The Journal of Privacy and Confidentiality, 2013)
This paper considers the scenario that all data entries in a confidentialised unit record file were masked by multiplicative noises, regardless of whether unit records are sensitive or not and regardless of whether the masked variables are dependent or independent variables in the underlying regression analysis. A technique is introduced in this paper to show how to estimate parameters in a regression model, which is originally fitted by unmasked data, based on masked data. Several simulation studies and a real-life data application are presented.