arXiv Machine Learning June 16, 2026 By Rob Romijnders, Antti Koskela

Convex Approximation of Two-Layer ReLU Networks for Hidden State Differential Privacy

Read the original on arXiv Machine Learning →

arXiv:2407. 04884v4 Announce Type: replace Abstract: The hidden state threat model of differential privacy (DP) assumes that the adversary has access only to the final trained machine learning (ML) model, without seeing intermediate states during training.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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

arXiv Machine Learning

Jul 1

Private Rate-Constrained Optimization with Applications to Fair Learning

arXiv:2505. 22703v2 Announce Type: replace Abstract: Many problems in trustworthy ML can be expressed as constraints on prediction rates across subpopulations, including group fairness constraints (demographic parity, equalized odds, etc.

By Mohammad Yaghini, Tudor Cebere, Michael Menart, Aur\'elien Bellet, Nicolas Papernot

safety

arXiv Machine Learning

Jul 1

Efficient Public Verification of Private ML via Regularization

arXiv:2512. 04008v2 Announce Type: replace Abstract: Training with differential privacy (DP) guarantees dataset members that they cannot be identified by users of the released model.

By Zo\"e Ruha Bell, Anvith Thudi, Olive Franzese-McLaughlin, Nicolas Papernot, Shafi Goldwasser

safety

arXiv Machine Learning

Jul 28

DP-IVON-Gradsq: Differentially Private Squared-Gradient Improved Variational Online Newton

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.

By Nour Jamoussi, Ikram Dridi, Giuseppe Serra, Marios Kountouris

safety

arXiv Machine Learning

Jul 23

End-to-End Differential Privacy in Training Deep Neural Network Classifiers

arXiv:2607. 19580v1 Announce Type: new Abstract: Differentially private machine learning enables model training on sensitive data while ensuring that individual data is unlikely to be recoverable from the parameters of the resulting model.

By Huaiyuan Rao, Calvin Hawkins, Alexander Benvenuti, Matthew Hale

benchmarks safety

arXiv Machine Learning

Jun 26

Escaping Iterative Parameter-Space Noise: Differentially Private Learning with a Hypernetwork

arXiv:2606. 26772v1 Announce Type: new Abstract: Differentially private (DP) training of neural networks is often hindered by the large amount of noise required by gradient-based methods such as DP-SGD, which repeatedly inject high-dimensional noise in parameter space throughout training.

By Naoki Nishikawa, Shokichi Takakura, Satoshi Hasegawa

rag diffusion fine-tuning safety

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Differentially Private Neural Network Training Under the Hidden State Assumption

Private Rate-Constrained Optimization with Applications to Fair Learning

Efficient Public Verification of Private ML via Regularization

DP-IVON-Gradsq: Differentially Private Squared-Gradient Improved Variational Online Newton

End-to-End Differential Privacy in Training Deep Neural Network Classifiers

Escaping Iterative Parameter-Space Noise: Differentially Private Learning with a Hypernetwork