arXiv:2508. 18037v2 Announce Type: replace Abstract: Leveraging information from public data has become increasingly crucial in enhancing the utility of differentially private (DP) methods.
arXiv:2609.39629v1 Announce Type: new Abstract: Differential privacy (DP) in machine learning is typically achieved by adding noise to model parameters (private learning) or to model outputs (private...
arXiv:2606. 18773v1 Announce Type: cross Abstract: We study differentially private (DP) regression in settings where each data sample includes public, non-sensitive features -- common in applications such as recommendation and advertising systems.
arXiv:2505. 14251v2 Announce Type: replace Abstract: We study the problem of differentially private second moment estimation and present a new algorithm that achieve strong privacy-utility trade-offs even for worst-case inputs under subsamplability assumptions on the data.
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:2407. 08233v3 Announce Type: replace Abstract: Current differentially private learning paradigms face a severe utility bottleneck: DP-SGD degrades performance through noise accumulation over training steps, while aggregation-based approaches such as PATE suffer from data inefficiency due to disjoint data partitioning.
arXiv:2303. 07152v3 Announce Type: replace-cross Abstract: Achieving optimal statistical performance while ensuring the privacy of personal data is a challenging yet crucial objective in modern data analysis.
arXiv:2606. 12654v2 Announce Type: replace-cross Abstract: We develop a new, differentially private mean estimator called the balloon mean.
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:2607. 23649v1 Announce Type: new Abstract: Differential privacy provides formal privacy guarantees for training neural networks on sensitive data, while Bayesian deep learning offers a principled framework for uncertainty-aware prediction.
The paper investigates high‑dimensional LASSO under differential privacy using objective perturbation when covariates have heterogeneous scales. It introduces a Gram‑based anisotropic objective perturbation that counteracts the distortion caused by covariate heterogeneity, restoring isotropy in the estimation process. Through an Approximate Message Passing framework and state evolution analysis, the authors show that this approach stabilizes convergence and improves both statistical efficiency and privacy performance compared to standard uniform noise injection.
arXiv:2605.30600v2 Announce Type: replace Abstract: Randomized sketching is a central tool for compressing large-scale optimization problems while preserving accuracy. In particular, sketches that ar...