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Noise Multiplication for Statistical Disclosure Control of Extreme Values in Log-normal Regression Samples
Noise Multiplication for Statistical Disclosure Control of Extreme Values in Log-normal Regression Samples
CDAR2014-04
Introduction
Statistical agencies must control disclosure risk when releasing data to the public. If income data on individuals or businesses are released, it could be possible to match extremely large values to specific individuals or businesses that are known to be wealthy, especially if some additional information is available on the same units in the dataset. The purpose of the present investigation is to explore noise multiplication as a strategy to protect large values in a dataset from disclosure, and to develop methodology for analyzing the resulting data under the assumption of a log-normal distribution on the sensitive variable. We assume that the log-scale mean of the sensitive variable is described by a linear regression on a set of non-sensitive covariates, and that the goal of the data analysis is to draw inference on parameters in the regression. We focus on the log-normal distribution because it is well known to be appropriate for modeling income data ([6]; [8]; [11]; [20]), and for income data, the extreme values usually need disclosure protection.
Others in Series
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Disclosure Avoidance Techniques at the U.S. Census Bureau
The U.S. Census Bureau collects its survey and census data under the U.S. Code’s Title 13, which promises confidentiality to its respondents.
Working Paper
Likelihood Based Inference Under Noise Multiplication
When statistical agencies release microdata to the public, a major concern is the control of disclosure risk, while ensuring utility in the released data.
Working Paper
Likelihood-Based Finite Sample Inference
Likelihood-based finite sample inference based on synthetic data under the exponential model is developed in this paper.
Page Last Revised - October 8, 2021
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