The paper investigates parametric change‑point detection under local differential privacy, concentrating on offline localization of a single change point in independent observations. It derives improved finite‑sample accuracy guarantees for a generalized likelihood‑ratio estimator using martingale methods and establishes finite‑sample minimax lower bounds in both non‑private and locally private settings, linking localization accuracy to Chernoff information. Guided by these bounds, the authors design locally private algorithms based on aggregated randomized response and binary mechanisms, optimize Chernoff information in different privacy regimes, and provide finite‑sample guarantees, while also computing strong data‑processing inequality coefficients for certain divergences and validating the results empirically on synthetic data.
arXiv:2606. 01256v1 Announce Type: cross Abstract: This paper introduces a distribution-free framework for constructing post-detection confidence sets for changepoints after stopping a sequential change detection procedure.
arXiv:2609.22642v1 Announce Type: cross Abstract: We develop inference for manifold-valued population minimizers when each observation belongs to a different participant and only locally private mess...
arXiv:2601. 21959v2 Announce Type: replace-cross Abstract: We develop a near-optimal testing procedure under the framework of Gaussian differential privacy for simple as well as one- and two-sided tests under monotone likelihood ratio conditions.
arXiv:2602. 01607v3 Announce Type: replace-cross Abstract: Differentially private synthetic data enables the sharing and analysis of sensitive datasets while providing rigorous privacy guarantees for individual contributors.
arXiv:2607. 29675v1 Announce Type: cross Abstract: Density modes provide a localized and interpretable summary of multimodal distributions, but their estimation under rigorous differential privacy constraints remains largely unexplored.
arXiv:2610.08460v1 Announce Type: new Abstract: We propose a simple framework for constructing differentially private confidence regions \textit{in one shot}, i.e., by adding noise only to the final...
The paper presents a Gaussian approximation for the privacy loss distribution in differentially private stochastic gradient descent (DP‑SGD) when subsampling is used. It shows that, after truncating rare large losses, the privacy loss behaves like a Gaussian with the same mean and variance, up to a small shift that grows only logarithmically with the error tolerance. This leads to tighter variance‑based bounds for subsampled mechanisms, improving upon previous Renyi‑divergence analyses and explaining the effectiveness of the connect‑the‑dots method for random allocation sampling.
arXiv:2609. 08234v1 Announce Type: cross Abstract: Suppose we are given an ordered sequence of independent data whose distribution changes $K$ times at unknown locations, for some unknown $K \geq 0$.
arXiv:2508.04800v2 Announce Type: replace-cross Abstract: We introduce a novel privatization framework for high-dimensional controlled variable selection. Our framework enables rigorous False Discove...
arXiv:2606. 12654v2 Announce Type: replace-cross Abstract: We develop a new, differentially private mean estimator called the balloon mean.
The paper introduces SPLICE, a differentially private synthetic data algorithm that uses noisy sliced Chebyshev polynomial moments to fit a distribution. SPLICE achieves a Sliced Wasserstein-1 distance of ∼O(1/√n) from the target distribution, a bound that does not depend on the data dimension. The authors also prove a new moment‑matching result and provide a practical implementation that outperforms existing DP synthetic data methods on a large benchmark.
arXiv:2602. 17284v2 Announce Type: replace Abstract: We consider the privacy amplification properties of a sampling scheme in which a user's data isused in $k$ steps chosen randomly and uniformly from a sequence (or set) of $t$ steps.