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...
By Yuxuan Tao, Adel Javanmard
arXiv:2602. 06838v3 Announce Type: replace Abstract: Federated learning enables collaborative model training across distributed clients while preserving data privacy.
By Jin Wang, Hui Ma, Yajun Zhang, Xinjun Pei, Ming Yan, Fei Xing, Yikun Chen
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.
By Haruka Tanzawa, Ayaka Sakata
The paper introduces Differential Privacy Representation Geometry for Medical Imaging (DP‑RGMI), a framework that interprets differential privacy as a structured transformation of representation space. DP‑RGMI decomposes performance loss into encoder geometry—measured by representation displacement and spectral effective dimension—and task‑head utilization, quantified by the gap between linear‑probe and end‑to‑end utility. Across 594,000 chest X‑ray images from four datasets, the study finds that differential privacy consistently creates a utilization gap even when linear separability remains, while displacement and spectral dimension vary non‑monotonically with initialization and dataset, indicating that privacy alters representation anisotropy rather than uniformly collapsing features.
By Soroosh Tayebi Arasteh, Marziyeh Mohammadi, Sven Nebelung, Daniel Truhn
arXiv:2606. 17995v1 Announce Type: cross Abstract: We study the privacy of releasing posterior sample paths from a Gaussian process (GP) when the entire training set including covariates and responses is private.
By Tomasz Maciazek
The paper investigates how differential privacy (DP) affects the ability to merge independently fine‑tuned task models into a single multi‑task model. It identifies two geometric challenges—local sharpness and reference drift—that hinder mergeability of private models. To address these, the authors propose DP‑Merging, a framework that uses a sharpness‑aware objective and a reference‑based alignment regularizer to improve mergeability while maintaining DP guarantees.
By Jin Liu, Junkang Liu, Ning Xi, Yinbin Miao, Dawei Wei, Ke Cheng, Jianfeng Ma