Debiasing Functions of Private Statistics in Postprocessing

This research paper explores how to create unbiased estimators from statistics protected by differential privacy, focusing on Laplace noise—a widely used mechanism in privacy-preserving data analysis. The authors develop mathematical techniques to correct for bias that typically arises when estimating nonlinear functions (like ratios) from noisy statistics. Their method is particularly useful in settings where privacy budgets are limited, such as government statistical releases. Applications include accurate, private estimates of means, ratios, and other commonly used statistics—critical for ensuring reliable data-driven decisions without compromising individual privacy.