arXiv:2610. 30196v1 Announce Type: cross Abstract: We study $(\varepsilon,\delta)$-differentially private synthetic data for arbitrary databases of $n$ records in $[-1,1]^d$ under a specified integral probability metric (IPM).
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.
The paper introduces SPLICE, a differentially private synthetic data algorithm that uses noisy sliced Chebyshev polynomial moments to fit a distribution. SPLICE achieves a Sliced Wasserstein-1 distance of ∼O(1/√n) from the target distribution, a bound that does not depend on the data dimension. The authors also prove a new moment‑matching result and provide a practical implementation that outperforms existing DP synthetic data methods on a large benchmark.
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,...
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...
The paper presents a Gaussian approximation for the privacy loss distribution in differentially private stochastic gradient descent (DP‑SGD) when subsampling is used. It shows that, after truncating rare large losses, the privacy loss behaves like a Gaussian with the same mean and variance, up to a small shift that grows only logarithmically with the error tolerance. This leads to tighter variance‑based bounds for subsampled mechanisms, improving upon previous Renyi‑divergence analyses and explaining the effectiveness of the connect‑the‑dots method for random allocation sampling.
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:2505. 14251v2 Announce Type: replace Abstract: We study the problem of differentially private second moment estimation and present a new algorithm that achieve strong privacy-utility trade-offs even for worst-case inputs under subsamplability assumptions on the data.
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.
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.
arXiv:2607. 29100v1 Announce Type: new Abstract: Differentially private (DP) training of text-conditioned generative models suffers a utility cliff at strong privacy.
The paper investigates parametric change‑point detection under local differential privacy, concentrating on offline localization of a single change point in independent observations. It derives improved finite‑sample accuracy guarantees for a generalized likelihood‑ratio estimator using martingale methods and establishes finite‑sample minimax lower bounds in both non‑private and locally private settings, linking localization accuracy to Chernoff information. Guided by these bounds, the authors design locally private algorithms based on aggregated randomized response and binary mechanisms, optimize Chernoff information in different privacy regimes, and provide finite‑sample guarantees, while also computing strong data‑processing inequality coefficients for certain divergences and validating the results empirically on synthetic data.
The paper analyzes deep nonparametric regression trained with stochastic gradient Langevin dynamics (SGLD), a stochastic gradient method that adds Gaussian noise. Using a PAC‑Bayesian framework for deep ReLU networks, it derives an excess‑risk bound that incorporates network approximation error, statistical complexity, and the cumulative effect of the SGLD trajectory. The study also provides differential privacy guarantees for per‑sample clipped SGLD and demonstrates the method’s predictive performance and privacy‑utility trade‑offs through simulations and real data experiments.