Noise Multiplication for Statistical Disclosure Control of Extreme Values in Log-normal Regression Samples
In this article multiplication of original data values by random noise is suggested as a disclosure control strategy when only the top part of the data is sensitive, as is often the case with income data. The proposed method can serve as an alternative to top coding which is a standard method in th...
Main Authors: | Martin Klein, Thomas Mathew, Bimal Sinha |
---|---|
Format: | Article |
Language: | English |
Published: |
Labor Dynamics Institute
2014-06-01
|
Series: | The Journal of Privacy and Confidentiality |
Subjects: | |
Online Access: | https://journalprivacyconfidentiality.org/index.php/jpc/article/view/637 |
Similar Items
-
Likelihood Based Finite Sample Inference for Singly Imputed Synthetic Data Under the Multivariate Normal and Multiple Linear Regression Models
by: Martin Klein, et al.
Published: (2015-12-01) -
Statistical Analysis of Noise-Multiplied Data Using Multiple Imputation
by: Klein Martin, et al.
Published: (2013-06-01) -
Aplicación de los modelos de regresión tobit en la modelización de variables epidemiológicas censuradas Application of tobit regression models in modelling censored epidemiological variables
by: M. J. Bleda Hernández, et al.
Published: (2002-04-01) -
Fundamental properties of Synthetic O-D Generation Formulations and Solutions
by: Paramahamsan, Harinarayan
Published: (2014) -
Analysis of Agreement Between Two Long Ranked Lists
by: Sampath, Srinath
Published: (2013)