arXiv Machine Learning June 4, 2026 By Xingwei Liu, Yuexin Chen, Jin-Ting Zhang, Wangli Xu

Differentially Private Joint Independence Test

Read the original on arXiv Machine Learning →

arXiv:2503. 18721v3 Announce Type: replace-cross Abstract: Identification of joint dependence among several random vectors plays an important role in many statistical applications, where the data may contain sensitive or confidential information.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

safety

arXiv Machine Learning

Jul 30

Differentially Private Permutation Tests

arXiv:2310. 19043v3 Announce Type: replace-cross Abstract: Recent years have witnessed growing concerns about the privacy of sensitive data.

By Ilmun Kim, Antonin Schrab

safety

arXiv Machine Learning

Jun 2

Near-Optimal Private Tests for Simple and MLR Hypotheses

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.

By Yu-Wei Chen, Raghu Pasupathy, Jordan Awan

safety

arXiv Machine Learning

Sep 10

Differentially Private Model-X Knockoffs via Johnson-Lindenstrauss Transform

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

safety

arXiv Machine Learning

Aug 18

Score Attack: A Lower Bound Technique for Optimal Differentially Private Learning

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.

By T. Tony Cai, Yichen Wang, Linjun Zhang

safety

arXiv Machine Learning

Jul 7

Efficient privacy loss accounting for subsampling and random allocation

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.

By Vitaly Feldman, Moshe Shenfeld

safety

arXiv Machine Learning

Jul 23

Differentially Private Neural Network Training Under the Hidden State Assumption

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.

By Ding Chen, Haochen Luo, Xiaofei Wang, Chen Liu

benchmarks safety

One email a morning, machine-written

Related stories

Differentially Private Permutation Tests

Near-Optimal Private Tests for Simple and MLR Hypotheses

Differentially Private Model-X Knockoffs via Johnson-Lindenstrauss Transform

Score Attack: A Lower Bound Technique for Optimal Differentially Private Learning

Efficient privacy loss accounting for subsampling and random allocation

Differentially Private Neural Network Training Under the Hidden State Assumption