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.
By Arkajyoti Bhattacharjee, Arnab Auddy
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.
By Nour Jamoussi, Ikram Dridi, Giuseppe Serra, Marios Kountouris
arXiv:2603. 19703v2 Announce Type: replace-cross Abstract: Estimating covariance matrices is fundamental to a wide range of statistical applications.
By T. Tony Cai, Yicheng Li
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:2504.00919v3 Announce Type: replace-cross
Abstract: We study the problem of estimating the spectral density of a centered stationary Gaussian time series under local differential privacy constr...
By Cristina Butucea, Karolina Klockmann, Tatyana Krivobokova
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.
By Bar Mahpud, Or Sheffet