arXiv Machine Learning September 11, 2026 By Anand Brahmbhatt, Rishi Saket, Shreyas Havaldar, Anshul Nasery, Yukti Makhija, Aravindan Raghuveer

Label Differential Privacy via Aggregation

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arXiv:2310. 10092v4 Announce Type: replace Abstract: This paper explores the use of linear aggregation to protect the privacy of sensitive training labels through the concept of \emph{label differential privacy} (label-DP) while maintaining regression task utility.

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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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arXiv Machine Learning

Aug 11

Fundamental Limitations of Favorable Privacy-Utility Guarantees for DP-SGD

arXiv:2601. 10237v3 Announce Type: replace Abstract: Differentially Private Stochastic Gradient Descent (DP-SGD) is the dominant paradigm for private training, but its fundamental limitations under worst-case adversarial privacy definitions remain poorly understood.

By Murat Bilgehan Ertan, Marten van Dijk

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arXiv Machine Learning

Jun 15

Private Prediction via PAC Privacy

arXiv:2601. 14033v2 Announce Type: replace Abstract: Machine learning models are increasingly served behind APIs.

By Xiaochen Zhu, Mayuri Sridhar, Srinivas Devadas

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arXiv Machine Learning

Aug 20

Topology-Aware Differential Privacy in Hierarchical Federated Learning

The paper introduces Fulcrum, a topology‑aware differential privacy scheme for hierarchical federated learning that allocates noise based on the size and exposure of regional aggregation groups. By deriving a closed‑form exposure dispersion metric from region structure and weights, the method optimally balances privacy and utility, achieving up to 14.84% accuracy gains on image tasks and 12.16% on text tasks at ε = 0.99 compared to uniform noise allocation. The approach ensures each participant receives noise commensurate with its actual exposure, eliminating unnecessary privacy overhead.

By Murtaza Rangwala, Richard O. Sinnott, Rajkumar Buyya

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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

Jun 2

IntraShuffler: A Privacy Preserving Framework for Heterogeneous DP Federated Learning

arXiv:2606. 02563v1 Announce Type: new Abstract: Heterogeneous Differential Privacy (HDP) in Federated Learning (FL) allows clients to select individual privacy budgets ($\varepsilon_i$) according to institutional policies and data sensitivity.

By Farhin Farhad Riya, Olivera Kotevska, Jinyuan Stella Sun

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

Fundamental Limitations of Favorable Privacy-Utility Guarantees for DP-SGD

Private Prediction via PAC Privacy

Topology-Aware Differential Privacy in Hierarchical Federated Learning

Differentially Private Neural Network Training Under the Hidden State Assumption

IntraShuffler: A Privacy Preserving Framework for Heterogeneous DP Federated Learning