arXiv:2508.04800v2 Announce Type: replace-cross Abstract: We introduce a novel privatization framework for high-dimensional controlled variable selection. Our framework enables rigorous False Discove...
arXiv:2605. 05905v2 Announce Type: replace Abstract: Objective perturbation is a standard mechanism in differentially private empirical risk minimization.
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.
arXiv:2602. 01607v3 Announce Type: replace-cross Abstract: Differentially private synthetic data enables the sharing and analysis of sensitive datasets while providing rigorous privacy guarantees for individual contributors.
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.
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:2606. 01908v1 Announce Type: new Abstract: Test-time adaptation (TTA) can reduce error on new and different data by updating the model on these inputs during inference.
arXiv:2607. 29675v1 Announce Type: cross Abstract: Density modes provide a localized and interpretable summary of multimodal distributions, but their estimation under rigorous differential privacy constraints remains largely unexplored.
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.
The paper introduces Jacobian-Guided Anisotropic Noise Reshaping, a method that improves data utility under Local Differential Privacy by selectively reducing noise in task-relevant subspaces of data representations. It uses the Jacobian of a public downstream model to identify critical directions and reshapes isotropic LDP noise into an anisotropic distribution, preserving privacy while enhancing performance. Experiments on CIFAR-10-C show significant accuracy gains, especially for PrivUnit variants at ε=7.5.
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:2609. 05034v1 Announce Type: cross Abstract: We consider density estimation under the relaxed local differential privacy condition that the privatized distributions are $\alpha$-close in total variation distance.