The paper introduces a general framework for non‑interactive locally differentially private (LDP) learning of structured univariate distributions that can be approximated by piece‑wise polynomials. It achieves the first sample‑efficient LDP learners for several distribution classes, notably mixtures of log‑concave distributions such as Gaussian mixture models (GMMs), while providing agnostic guarantees and computational efficiency. The authors also establish a new lower bound for learning GMMs under LDP, surpassing previous pure‑DP packing bounds, and develop an efficient non‑interactive LDP algorithm for synthetic data generation over large domains, which is then combined with a deterministic adaptation of a non‑private piecewise polynomial learner.
arXiv:2609.36678v1 Announce Type: new Abstract: Private Evolution (PE) is a differentially private algorithm for synthetic data generation. While it can be viewed as a Wasserstein learning algorithm,...
The paper proposes a method for releasing Gaussian Mixture Models (GMMs) under differential privacy (DP) by minimizing Kullback‑Leibler (KL) divergence between the released and non‑private models. It jointly optimizes privacy‑budget allocation and parameter perturbations for mixture weights, means, and covariances, providing a tractable expression for KL divergence and a theoretical analysis of DP guarantees. Experiments on synthetic and real data show that the approach achieves strong privacy while preserving high utility.
Private Evolution (PE) is a differentially private algorithm for synthetic data generation. While it can be viewed as a Wasserstein learning algorithm, it performs much better in practice than worst-c...
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:2608. 02480v1 Announce Type: cross Abstract: With AI systems gaining more access to individuals' information, it is important to protect privacy when reporting statistical answers.
With AI systems gaining more access to individuals' information, it is important to protect privacy when reporting statistical answers. Equally important is to privatize the reporting of uncertainty in such answers.
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.
The paper extends the abstract gradient training (AGT) framework to provide tighter differential privacy guarantees for both private prediction and private learning. It introduces Abstract Gradient Sampling (AGS) to analyze privacy in continuous, unbounded regression and offers theoretical and empirical evidence that these methods yield tighter bounds than global-sensitivity baselines, even in previously unbounded settings. The authors also demonstrate that their private learning algorithm can outperform standard private learners under comparable conditions.
arXiv:2609. 28695v1 Announce Type: new Abstract: This work presents a federated framework for training Averaged $n$-Dependence Estimators (AnDE) in distributed environments.
arXiv:2606. 11283v1 Announce Type: cross Abstract: We study the problem of generating synthetic data under differential privacy.
arXiv:2610.33712v1 Announce Type: new Abstract: Federated learning trains a shared model across clients without exchanging their data, yet the updates they transmit still leak information about indiv...
We revisit the fairness notion of disparate impact for synthetic data generation (SDG), that assesses whether the utility of generated records is the same across sensitive groups. Our approach departs from existing work on fair SDG, that address the problem of correcting for undue biases in the observed distribution, hence redefining SDG as learning a distribution that is not that of the real data.