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.
By Rundong Ding, Yiyun He, Yizhe Zhu
arXiv:2511. 13999v2 Announce Type: replace Abstract: We study the running time, in terms of first order oracle queries, of differentially private empirical/population risk minimization of Lipschitz convex losses.
By Michael Menart, Aleksandar Nikolov
arXiv:2604. 10819v2 Announce Type: replace-cross Abstract: A recent line of work initiated by Chiesa and Gur and further developed by Herman and Rothblum investigates the sample and communication complexity of verifying properties of distributions with the assistance of a powerful, knowledgeable, but untrusted prover.
By Elbert Du, Cynthia Dwork, Pranay Tankala, Linjun Zhang
arXiv:2602. 17284v2 Announce Type: replace Abstract: We consider the privacy amplification properties of a sampling scheme in which a user's data isused in $k$ steps chosen randomly and uniformly from a sequence (or set) of $t$ steps.
By Vitaly Feldman, Moshe Shenfeld
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
arXiv:2607. 07209v1 Announce Type: cross Abstract: Modern federated and streaming learning systems often release intermediate models, so privacy must hold for the full trajectory under adaptive interaction.
By T-H. Hubert Chan, Elaine Shi, Mengshi Zhao, Mingxun Zhou