mailitics

Tag: privacy

  • Generalized Leverage Score for Scalable Assessment of Privacy Vulnerability

    Generalized Leverage Score for Scalable Assessment of Privacy Vulnerability arXiv:2602.15919v1 Announce Type: new Abstract: Can the privacy vulnerability of individual data points be assessed without retraining models or explicitly simulating attacks? We answer affirmatively by showing that exposure to membership inference attack (MIA) is fundamentally governed by a data point’s influence on the learned model.…

  • Privacy utility trade offs for parameter estimation in degree heterogeneous higher order networks

    Privacy utility trade offs for parameter estimation in degree heterogeneous higher order networks arXiv:2602.03948v1 Announce Type: new Abstract: In sensitive applications involving relational datasets, protecting information about individual links from adversarial queries is of paramount importance. In many such settings, the available data are summarized solely through the degrees of the nodes in the network.…

  • High-Probability Bounds For Heterogeneous Local Differential Privacy

    High-Probability Bounds For Heterogeneous Local Differential Privacy arXiv:2510.11895v1 Announce Type: new Abstract: We study statistical estimation under local differential privacy (LDP) when users may hold heterogeneous privacy levels and accuracy must be guaranteed with high probability. Departing from the common in-expectation analyses, and for one-dimensional and multi-dimensional mean estimation problems, we develop finite sample upper…

  • Differentially Private Two-Stage Gradient Descent for Instrumental Variable Regression

    Differentially Private Two-Stage Gradient Descent for Instrumental Variable Regression arXiv:2509.22794v1 Announce Type: new Abstract: We study instrumental variable regression (IVaR) under differential privacy constraints. Classical IVaR methods (like two-stage least squares regression) rely on solving moment equations that directly use sensitive covariates and instruments, creating significant risks of privacy leakage and posing challenges in designing…

  • Privacy Auditing Synthetic Data Release through Local Likelihood Attacks

    Privacy Auditing Synthetic Data Release through Local Likelihood Attacks arXiv:2508.21146v1 Announce Type: cross Abstract: Auditing the privacy leakage of synthetic data is an important but unresolved problem. Most existing privacy auditing frameworks for synthetic data rely on heuristics and unreasonable assumptions to attack the failure modes of generative models, exhibiting limited capability to describe and…

  • Rao Differential Privacy

    Rao Differential Privacy arXiv:2508.17135v1 Announce Type: new Abstract: Differential privacy (DP) has recently emerged as a definition of privacy to release private estimates. DP calibrates noise to be on the order of an individuals contribution. Due to the this calibration a private estimate obscures any individual while preserving the utility of the estimate. Since the…

  • 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…

  • Differential Privacy in Kernelized Contextual Bandits via Random Projections

    Differential Privacy in Kernelized Contextual Bandits via Random Projections arXiv:2507.13639v1 Announce Type: new Abstract: We consider the problem of contextual kernel bandits with stochastic contexts, where the underlying reward function belongs to a known Reproducing Kernel Hilbert Space. We study this problem under an additional constraint of Differential Privacy, where the agent needs to ensure…

  • GRAND: Graph Release with Assured Node Differential Privacy

    GRAND: Graph Release with Assured Node Differential Privacy arXiv:2507.00402v1 Announce Type: new Abstract: Differential privacy is a well-established framework for safeguarding sensitive information in data. While extensively applied across various domains, its application to network data — particularly at the node level — remains underexplored. Existing methods for node-level privacy either focus exclusively on query-based…

  • 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…

  • Privacy-Preserving Transfer Learning for Community Detection using Locally Distributed Multiple Networks

    Privacy-Preserving Transfer Learning for Community Detection using Locally Distributed Multiple Networks arXiv:2504.00890v1 Announce Type: new Abstract: This paper develops a new spectral clustering-based method called TransNet for transfer learning in community detection of network data. Our goal is to improve the clustering performance of the target network using auxiliary source networks, which are heterogeneous, privacy-preserved,…

  • Optimal Survey Design for Private Mean Estimation

    Optimal Survey Design for Private Mean Estimation arXiv:2501.18121v1 Announce Type: new Abstract: This work identifies the first privacy-aware stratified sampling scheme that minimizes the variance for general private mean estimation under the Laplace, Discrete Laplace (DLap) and Truncated-Uniform-Laplace (TuLap) mechanisms within the framework of differential privacy (DP). We view stratified sampling as a subsampling operation,…

  • Mixing Times and Privacy Analysis for the Projected Langevin Algorithm under a Modulus of Continuity

    Mixing Times and Privacy Analysis for the Projected Langevin Algorithm under a Modulus of Continuity arXiv:2501.04134v1 Announce Type: new Abstract: We study the mixing time of the projected Langevin algorithm (LA) and the privacy curve of noisy Stochastic Gradient Descent (SGD), beyond nonexpansive iterations. Specifically, we derive new mixing time bounds for the projected LA…

  • $(epsilon, delta)$-Differentially Private Partial Least Squares Regression

    $(epsilon, delta)$-Differentially Private Partial Least Squares Regression arXiv:2412.09164v1 Announce Type: new Abstract: As data-privacy requirements are becoming increasingly stringent and statistical models based on sensitive data are being deployed and used more routinely, protecting data-privacy becomes pivotal. Partial Least Squares (PLS) regression is the premier tool for building such models in analytical chemistry, yet it…