Time-uniform and Asymptotic Confidence Sequence of Quantile under Local Differential Privacy

Leheng Cai, Qirui Hu, Juntao Sun, Shuyuan Wu

Keywords: Confidence sequence, local differential privacy, quantile, stochastic gradient decent

TL;DR: In this paper, we develop a novel algorithm for constructing time‑uniform, asymptotic confidence sequences for quantiles under local differential privacy.

Abstract: In this paper, we develop a novel algorithm for constructing time-uniform, asymptotic confidence sequences for quantiles under local differential privacy (LDP). The procedure combines dynamically chained parallel stochastic gradient descent (P-SGD) with a randomized response mechanism, thereby guaranteeing privacy protection while simultaneously estimating the target quantile and its variance. A strong Gaussian approximation for the proposed estimator yields asymptotically anytime-valid confidence sequences whose widths obey the law of the iterated logarithm (LIL). Moreover, the method is fully online, offering high computational efficiency and requiring only $\mathcal{O}(\kappa)$ memory, where $\kappa$ denotes the number of chains and is much smaller than the sample size. Rigorous mathematical proofs and extensive numerical experiments demonstrate the theoretical soundness and practical effectiveness of the algorithm.

Primary Area: Social and economic aspects of machine learning (e.g., fairness, interpretability, human-AI interaction, privacy, safety, strategic behavior)

Submission Number: 22347