arXiv Machine Learning
Jul 31
Robust Estimation of Sparse Numerical Vectors under Local Differential Privacy
arXiv:2607. 27815v1 Announce Type: cross Abstract: Local differential privacy (LDP) protocols are vulnerable to poisoning attacks.
ABC: Numerical Data Collection under Local Differential Privacy without Prior Knowledge
arXiv:2608. 05737v1 Announce Type: cross Abstract: Local Differential Privacy (LDP) provides strong privacy guarantees for collecting numerical data.
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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.
arXiv AI
Jul 9
An Adaptive Differentially Private Federated Learning Framework
arXiv:2602. 06838v3 Announce Type: replace Abstract: Federated learning enables collaborative model training across distributed clients while preserving data privacy.
arXiv Machine Learning
Jun 4
DPDL: Towards Differential Privacy Preservation in Decentralized Stochastic Learning on Non-IID Data
arXiv:2606. 04399v1 Announce Type: new Abstract: In the paradigm of decentralized learning, a group of agents collaborate to train a global model using distributed datasets without a central server.
arXiv Machine Learning
Jul 13
LDPKiT: Superimposing Remote Queries for Privacy-Preserving Distillation
arXiv:2405. 16361v4 Announce Type: replace Abstract: To protect privacy in regulated domains such as healthcare and finance, model owners may allow only remote API access while keeping both the training data and model parameters private.
arXiv AI
Jul 13
How to DP-fy Your Data: A Practical Guide to Generating Synthetic Data With Differential Privacy
arXiv:2512. 03238v2 Announce Type: replace-cross Abstract: High quality data is needed to unlock the full potential of AI for end users.
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 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.
arXiv Machine Learning
Jun 30
Efficient Unlearning with Privacy Guarantees
arXiv:2507. 04771v2 Announce Type: replace-cross Abstract: Privacy protection laws, such as the GDPR, grant individuals the right to request the forgetting of their personal data not only from databases but also from machine learning (ML) models trained on them.
arXiv Machine Learning
Jun 3
Unlearning with Asymmetric Sources: Improved Unlearning-Utility Trade-off with Public Data
arXiv:2605. 11170v2 Announce Type: replace Abstract: Noise-based certified machine unlearning currently faces a hard ceiling: the noise magnitude required to certify unlearning typically destroys model utility, particularly for large-scale deletion requests.
Hugging Face Trending Papers
Jun 1
IntraShuffler: A Privacy Preserving Framework for Heterogeneous DP Federated Learning
Heterogeneous Differential Privacy (HDP) in Federated Learning (FL) allows clients to select individual privacy budgets ($\varepsilon_i$) according to institutional policies and data sensitivity. In practice, many HDP-FL systems employ $\varepsilon$-aware server aggregation to improve model utility by re-weighting client updates according to their declared privacy budgets.
arXiv Machine Learning
Jun 8
The Utility and Complexity of in- and out-of-Distribution Machine Unlearning
arXiv:2412. 09119v3 Announce Type: replace Abstract: Machine unlearning, the process of selectively removing data from trained models, is increasingly crucial for addressing privacy concerns and knowledge gaps post-deployment.