mailitics

Tag: private

  • Locally Private Parametric Methods for Change-Point Detection

    Locally Private Parametric Methods for Change-Point Detection arXiv:2602.13619v1 Announce Type: new Abstract: We study parametric change-point detection, where the goal is to identify distributional changes in time series, under local differential privacy. In the non-private setting, we derive improved finite-sample accuracy guarantees for a change-point detection algorithm based on the generalized log-likelihood ratio test, via…

  • Differentially Private High-dimensional Variable Selection via Integer Programming

    Differentially Private High-dimensional Variable Selection via Integer Programming arXiv:2510.22062v1 Announce Type: new Abstract: Sparse variable selection improves interpretability and generalization in high-dimensional learning by selecting a small subset of informative features. Recent advances in Mixed Integer Programming (MIP) have enabled solving large-scale non-private sparse regression – known as Best Subset Selection (BSS) – with millions…

  • Private Realizable-to-Agnostic Transformation with Near-Optimal Sample Complexity

    Private Realizable-to-Agnostic Transformation with Near-Optimal Sample Complexity arXiv:2510.01291v1 Announce Type: new Abstract: The realizable-to-agnostic transformation (Beimel et al., 2015; Alon et al., 2020) provides a general mechanism to convert a private learner in the realizable setting (where the examples are labeled by some function in the concept class) to a private learner in the agnostic…

  • Private Learning of Littlestone Classes, Revisited

    Private Learning of Littlestone Classes, Revisited arXiv:2510.00076v1 Announce Type: new Abstract: We consider online and PAC learning of Littlestone classes subject to the constraint of approximate differential privacy. Our main result is a private learner to online-learn a Littlestone class with a mistake bound of $tilde{O}(d^{9.5}cdot log(T))$ in the realizable case, where $d$ denotes the…

  • Differentially Private Decentralized Dataset Synthesis Through Randomized Mixing with Correlated Noise

    Differentially Private Decentralized Dataset Synthesis Through Randomized Mixing with Correlated Noise arXiv:2509.10385v1 Announce Type: new Abstract: In this work, we explore differentially private synthetic data generation in a decentralized-data setting by building on the recently proposed Differentially Private Class-Centric Data Aggregation (DP-CDA). DP-CDA synthesizes data in a centralized setting by mixing multiple randomly-selected samples from…

  • Differentially Private Model-X Knockoffs via Johnson-Lindenstrauss Transform

    Differentially Private Model-X Knockoffs via Johnson-Lindenstrauss Transform arXiv:2508.04800v1 Announce Type: new Abstract: We introduce a novel privatization framework for high-dimensional controlled variable selection. Our framework enables rigorous False Discovery Rate (FDR) control under differential privacy constraints. While the Model-X knockoff procedure provides FDR guarantees by constructing provably exchangeable “negative control” features, existing privacy mechanisms like…

  • High-Dimensional Differentially Private Quantile Regression: Distributed Estimation and Statistical Inference

    High-Dimensional Differentially Private Quantile Regression: Distributed Estimation and Statistical Inference arXiv:2508.05212v1 Announce Type: new Abstract: With the development of big data and machine learning, privacy concerns have become increasingly critical, especially when handling heterogeneous datasets containing sensitive personal information. Differential privacy provides a rigorous framework for safeguarding individual privacy while enabling meaningful statistical analysis. In…

  • Differentially private ratio statistics

    Differentially private ratio statistics arXiv:2505.20351v1 Announce Type: new Abstract: Ratio statistics–such as relative risk and odds ratios–play a central role in hypothesis testing, model evaluation, and decision-making across many areas of machine learning, including causal inference and fairness analysis. However, despite privacy concerns surrounding many datasets and despite increasing adoption of differential privacy, differentially private…

  • Gaussian Differential Private Bootstrap by Subsampling

    Gaussian Differential Private Bootstrap by Subsampling arXiv:2505.01197v1 Announce Type: new Abstract: Bootstrap is a common tool for quantifying uncertainty in data analysis. However, besides additional computational costs in the application of the bootstrap on massive data, a challenging problem in bootstrap based inference under Differential Privacy consists in the fact that it requires repeated access…

  • How Private is Your Attention? Bridging Privacy with In-Context Learning

    How Private is Your Attention? Bridging Privacy with In-Context Learning arXiv:2504.16000v1 Announce Type: new Abstract: In-context learning (ICL)-the ability of transformer-based models to perform new tasks from examples provided at inference time-has emerged as a hallmark of modern language models. While recent works have investigated the mechanisms underlying ICL, its feasibility under formal privacy constraints…