arXiv Machine Learning
Differentially Private Model-X Knockoffs via Johnson-Lindenstrauss Transform
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A Sharp Transition in Data Reconstruction under Differential Privacy
arXiv:2609.37344v1 Announce Type: cross Abstract: Data reconstruction attacks have empirically been successful in recovering training samples from learned models, raising privacy concerns and motivat...
arXiv Machine Learning
Oct 5
High-Dimensional Asymptotics and Dataset Selection for Private Transfer Learning
The paper addresses the challenge of selecting external datasets for private transfer learning by modeling high‑dimensional regression with heterogeneous sources and a weighted ridge estimator. It relies solely on aggregated statistics and offers privacy guarantees under $ ho$‑zero‑concentrated differential privacy for labels or both features and labels. A deterministic equivalent of test error is derived, enabling optimization of hyperparameters and decision‑making about the utility of private external data without accessing individual records.
arXiv Machine Learning
Oct 5
High-Dimensional Asymptotics of Differentially Private PCA
arXiv:2511. 07270v4 Announce Type: replace-cross Abstract: In differential privacy, random noise is introduced to privatize summary statistics of a sensitive dataset before releasing them.
arXiv Machine Learning
Oct 16
Differentially Private Distribution Release of Gaussian Mixture Models via KL-Divergence Minimization
The paper proposes a method for releasing Gaussian Mixture Models (GMMs) under differential privacy (DP) by minimizing Kullback‑Leibler (KL) divergence between the released and non‑private models. It jointly optimizes privacy‑budget allocation and parameter perturbations for mixture weights, means, and covariances, providing a tractable expression for KL divergence and a theoretical analysis of DP guarantees. Experiments on synthetic and real data show that the approach achieves strong privacy while preserving high utility.
arXiv Machine Learning
Jun 4
Revisiting Privacy Amplification by Subsampling in Selective Release DPSGD
arXiv:2606. 04384v1 Announce Type: new Abstract: Machine learning's reliance on sensitive data necessitates privacy-preserving techniques like Differentially Private Stochastic Gradient Descent (DPSGD).
arXiv AI
Jun 17
Gaussian DP for Reporting Differential Privacy Guarantees in Machine Learning
arXiv:2503. 10945v3 Announce Type: replace-cross Abstract: Current practices for reporting differential privacy (DP) guarantees for machine learning (ML) algorithms such as DP-SGD provide an incomplete and potentially misleading picture.