arXiv Machine Learning July 28, 2026 By Nour Jamoussi, Ikram Dridi, Giuseppe Serra, Marios Kountouris

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

Read the original on arXiv Machine Learning →

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.

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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

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arXiv AI

Jul 8

Differentially Private Natural Gradient Descent

arXiv:2607. 05866v1 Announce Type: cross Abstract: Under a fixed privacy budget, the utility of differentially private (DP) training is ultimately determined by its optimization efficiency.

By Pan Li, Kai Chen, Shuai Chang, Shengzhi Zhang, Peizhuo Lv, Jinwen He

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arXiv Machine Learning

2d ago

Certification-Based Differentially Private Learning

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...

By Mihnea Ghitu, Matthew Wicker

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arXiv Machine Learning

Jul 27

Correlating Cross-Iteration Noise for DP-SGD using Model Curvature

arXiv:2510. 05416v3 Announce Type: replace Abstract: Differentially private stochastic gradient descent (DP-SGD) offers the promise of training deep learning models while mitigating many privacy risks.

By Xin Gu, Yingtai Xiao, Guanlin He, Jiamu Bai, Daniel Kifer, Kiwan Maeng

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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

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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

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