Differentially Private Inference for Longitudinal Linear Regression

TL;DR

This paper introduces a new framework for performing differentially private linear regression on longitudinal data, protecting entire user trajectories, with theoretical guarantees and practical effectiveness.

Contribution

It develops the first unified approach for user-level DP estimation and inference in longitudinal linear regression with dependent data.

Findings

Finite-sample guarantees and asymptotic normality established.

Proposed estimators are heteroskedasticity- and autocorrelation-consistent.

Empirical results show promising performance.

Abstract

Differential Privacy (DP) provides a rigorous framework for releasing statistics while protecting individual information present in a dataset. Although substantial progress has been made on differentially private linear regression, existing methods almost exclusively address the item-level DP setting, where each user contributes a single observation. Many scientific and economic applications instead involve longitudinal or panel data, in which each user contributes multiple dependent observations. In these settings, item-level DP offers inadequate protection, and user-level DP - shielding an individual's entire trajectory - is the appropriate privacy notion. We develop a comprehensive framework for estimation and inference in longitudinal linear regression under user-level DP. We propose a user-level private regression estimator based on aggregating local regressions, and we establish finite-sample guarantees and asymptotic normality under short-range dependence. For inference, we develop a privatized, bias-corrected covariance estimator that is automatically heteroskedasticity- and autocorrelation-consistent. These results provide the first unified framework for practical user-level DP estimation and inference in longitudinal linear regression under dependence, with strong theoretical guarantees and promising empirical performance.