Differential Privacy Papers
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Differential Privacy Papers
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new arXiv preprints mentioning "differential privacy" or "differentially private" in the title/abstract
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Differentially Private Fisher Randomization Tests for Binary Outcomes

Qingyang Sun, Jerome P. Reiter

http://arxiv.org/abs/2511.20884
Differentially Private Fisher Randomization Tests for Binary Outcomes Qingyang Sun, Jerome P. Reiter http://arxiv.org/abs/2511.20884 Across many disciplines, causal inference often relies on randomized experiments with binary outcomes. In such experiments, the Fisher randomization test provides exact, assumption-free tests for causal effects. Sometimes the outcomes are sensitive and must be kept confidential, for example, when they comprise physical or mental health measurements. Releasing test statistics or p-values computed with the confidential outcomes can leak information about the individuals in the study. Those responsible for sharing the analysis results may wish to bound this information leakage, which they can do by ensuring the released outputs satisfy differential privacy. In this article, we develop and compare several differentially private versions of the Fisher randomization test for binary outcomes. Specifically, we consider direct perturbation approaches that inject calibrated noise into test statistics or p-values, as well as a mechanism-aware, Bayesian denoising framework that explicitly models the privacy mechanism. We further develop decision-making procedures under privacy constraints, including a Bayes risk-optimal rule and a frequentist-calibrated significance test. Through theoretical results, simulation studies, and an application to the ADAPTABLE clinical trial, we demonstrate that our methods can achieve valid and interpretable causal inference while ensuring the differential privacy guarantee.
November 27, 2025 at 4:53 AM
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Road Network-Aware Personalized Trajectory Protection with Differential Privacy under Spatiotemporal Correlations

Minghui Min, Jiahui Liu, Mingge Cao, Shiyin Li, Hongliang Zhang, Miao Pan, Zhu Han

http://arxiv.org/abs/2511.21020
Road Network-Aware Personalized Trajectory Protection with Differential Privacy under Spatiotemporal Correlations Minghui Min, Jiahui Liu, Mingge Cao, Shiyin Li, Hongliang Zhang, Miao Pan, Zhu Han http://arxiv.org/abs/2511.21020 Location-Based Services (LBSs) offer significant convenience to mobile users but pose significant privacy risks, as attackers can infer sensitive personal information through spatiotemporal correlations in user trajectories. Since users' sensitivity to location data varies based on factors such as stay duration, access frequency, and semantic sensitivity, implementing personalized privacy protection is imperative. This paper proposes a Personalized Trajectory Privacy Protection Mechanism (PTPPM) to address these challenges. Our approach begins by modeling an attacker's knowledge of a user's trajectory spatiotemporal correlations, which enables the attacker to identify possible location sets and disregard low-probability location sets. To combat this, we integrate geo-indistinguishability with distortion privacy, allowing users to customize their privacy preferences through a configurable privacy budget and expected inference error bound. This approach provides the theoretical framework for constructing a Protection Location Set (PLS) that obscures users' actual locations. Additionally, we introduce a Personalized Privacy Budget Allocation Algorithm (PPBA), which assesses the sensitivity of locations based on trajectory data and allocates privacy budgets accordingly. This algorithm considers factors such as location semantics and road network constraints. Furthermore, we propose a Permute-and-Flip mechanism that generates perturbed locations while minimizing perturbation distance, thus balancing privacy protection and Quality of Service (QoS). Simulation results demonstrate that our mechanism outperforms existing benchmarks, offering superior privacy protection while maintaining user QoS requirements.
November 27, 2025 at 4:53 AM
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Data Exfiltration by Compression Attack: Definition and Evaluation on Medical Image Data

Huiyu Li, Nicholas Ayache, Hervé Delingette

http://arxiv.org/abs/2511.21227
Data Exfiltration by Compression Attack: Definition and Evaluation on Medical Image Data Huiyu Li, Nicholas Ayache, Hervé Delingette http://arxiv.org/abs/2511.21227 With the rapid expansion of data lakes storing health data and hosting AI algorithms, a prominent concern arises: how safe is it to export machine learning models from these data lakes? In particular, deep network models, widely used for health data processing, encode information from their training dataset, potentially leading to the leakage of sensitive information upon its export. This paper thoroughly examines this issue in the context of medical imaging data and introduces a novel data exfiltration attack based on image compression techniques. This attack, termed Data Exfiltration by Compression, requires only access to a data lake and is based on lossless or lossy image compression methods. Unlike previous data exfiltration attacks, it is compatible with any image processing task and depends solely on an exported network model without requiring any additional information to be collected during the training process. We explore various scenarios, and techniques to limit the size of the exported model and conceal the compression codes within the network. Using two public datasets of CT and MR images, we demonstrate that this attack can effectively steal medical images and reconstruct them outside the data lake with high fidelity, achieving an optimal balance between compression and reconstruction quality. Additionally, we investigate the impact of basic differential privacy measures, such as adding Gaussian noise to the model parameters, to prevent the Data Exfiltration by Compression Attack. We also show how the attacker can make their attack resilient to differential privacy at the expense of decreasing the number of stolen images. Lastly, we propose an alternative prevention strategy by fine-tuning the model to be exported.
November 27, 2025 at 4:53 AM
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Differentially Private Computation of the Gini Index for Income Inequality

Wenjie Lan, Jerome P. Reiter

http://arxiv.org/abs/2511.19771
Differentially Private Computation of the Gini Index for Income Inequality Wenjie Lan, Jerome P. Reiter http://arxiv.org/abs/2511.19771 The Gini index is a widely reported measure of income inequality. In some settings, the underlying data used to compute the Gini index are confidential. The organization charged with reporting the Gini index may be concerned that its release could leak information about the underlying data. We present an approach for bounding this information leakage by releasing a differentially private version of the Gini index. In doing so, we analyze how adding, deleting, or altering a single observation in any specific dataset can affect the computation of the Gini index; this is known as the local sensitivity. We then derive a smooth upper bound on the local sensitivity. Using this bound, we define a mechanism that adds noise to the Gini index, thereby satisfying differential privacy. Using simulated and genuine income data, we show that the mechanism can reduce the errors from noise injection substantially relative to differentially private algorithms that rely on the global sensitivity, that is, the maximum of the local sensitivities over all possible datasets. We characterize settings where using smooth sensitivity can provide highly accurate estimates, as well as settings where the noise variance is simply too large to provide reliably useful results. We also present a Bayesian post-processing step that provides interval estimates about the value of the Gini index computed with the confidential data.
November 26, 2025 at 4:53 AM
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N2E: A General Framework to Reduce Node-Differential Privacy to Edge-Differential Privacy for Graph Analytics

Yihua Hu, Hao Ding, Wei Dong

http://arxiv.org/abs/2511.20125
N2E: A General Framework to Reduce Node-Differential Privacy to Edge-Differential Privacy for Graph Analytics Yihua Hu, Hao Ding, Wei Dong http://arxiv.org/abs/2511.20125 Differential privacy (DP) has been widely adopted to protect sensitive information in graph analytics. While edge-DP, which protects privacy at the edge level, has been extensively studied, node-DP, offering stronger protection for entire nodes and their incident edges, remains largely underexplored due to its technical challenges. A natural way to bridge this gap is to develop a general framework for reducing node-DP graph analytical tasks to edge-DP ones, enabling the reuse of existing edge-DP mechanisms. A straightforward solution based on group privacy divides the privacy budget by a given degree upper bound, but this leads to poor utility when the bound is set conservatively large to accommodate worst-case inputs. To address this, we propose node-to-edge (N2E), a general framework that reduces any node-DP graph analytical task to an edge-DP one, with the error dependency on the graph's true maximum degree. N2E introduces two novel techniques: a distance-preserving clipping mechanism that bounds edge distance between neighboring graphs after clipping, and the first node-DP mechanism for maximum degree approximation, enabling tight, privacy-preserving clipping thresholds. By instantiating N2E with existing edge-DP mechanisms, we obtain the first node-DP solutions for tasks such as maximum degree estimation. For edge counting, our method theoretically matches the error of the state-of-the-art, which is provably optimal, and significantly outperforms existing approaches for degree distribution estimation. Experimental results demonstrate that our framework achieves up to a 2.5x reduction in error for edge counting and up to an 80x reduction for degree distribution estimation.
November 26, 2025 at 4:53 AM
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DP-MicroAdam: Private and Frugal Algorithm for Training and Fine-tuning

Mihaela Hudişteanu, Edwige Cyffers, Nikita P. Kalinin

http://arxiv.org/abs/2511.20509
DP-MicroAdam: Private and Frugal Algorithm for Training and Fine-tuning Mihaela Hudişteanu, Edwige Cyffers, Nikita P. Kalinin http://arxiv.org/abs/2511.20509 Adaptive optimizers are the de facto standard in non-private training as they often enable faster convergence and improved performance. In contrast, differentially private (DP) training is still predominantly performed with DP-SGD, typically requiring extensive compute and hyperparameter tuning. We propose DP-MicroAdam, a memory-efficient and sparsity-aware adaptive DP optimizer. We prove that DP-MicroAdam converges in stochastic non-convex optimization at the optimal $\mathcal{O}(1/\sqrt{T})$ rate, up to privacy-dependent constants. Empirically, DP-MicroAdam outperforms existing adaptive DP optimizers and achieves competitive or superior accuracy compared to DP-SGD across a range of benchmarks, including CIFAR-10, large-scale ImageNet training, and private fine-tuning of pretrained transformers. These results demonstrate that adaptive optimization can improve both performance and stability under differential privacy.
November 26, 2025 at 4:53 AM
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Learning Rate Scheduling with Matrix Factorization for Private Training

Nikita P. Kalinin, Joel Daniel Andersson

http://arxiv.org/abs/2511.17994
Learning Rate Scheduling with Matrix Factorization for Private Training Nikita P. Kalinin, Joel Daniel Andersson http://arxiv.org/abs/2511.17994 We study differentially private model training with stochastic gradient descent under learning rate scheduling and correlated noise. Although correlated noise, in particular via matrix factorizations, has been shown to improve accuracy, prior theoretical work focused primarily on the prefix-sum workload. That workload assumes a constant learning rate, whereas in practice learning rate schedules are widely used to accelerate training and improve convergence. We close this gap by deriving general upper and lower bounds for a broad class of learning rate schedules in both single- and multi-epoch settings. Building on these results, we propose a learning-rate-aware factorization that achieves improvements over prefix-sum factorizations under both MaxSE and MeanSE error metrics. Our theoretical analysis yields memory-efficient constructions suitable for practical deployment, and experiments on CIFAR-10 and IMDB datasets confirm that schedule-aware factorizations improve accuracy in private training.
November 25, 2025 at 4:54 AM
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Understanding Private Learning From Feature Perspective

Meng Ding, Mingxi Lei, Shaopeng Fu, Shaowei Wang, Di Wang, Jinhui Xu

http://arxiv.org/abs/2511.18006
Understanding Private Learning From Feature Perspective Meng Ding, Mingxi Lei, Shaopeng Fu, Shaowei Wang, Di Wang, Jinhui Xu http://arxiv.org/abs/2511.18006 Differentially private Stochastic Gradient Descent (DP-SGD) has become integral to privacy-preserving machine learning, ensuring robust privacy guarantees in sensitive domains. Despite notable empirical advances leveraging features from non-private, pre-trained models to enhance DP-SGD training, a theoretical understanding of feature dynamics in private learning remains underexplored. This paper presents the first theoretical framework to analyze private training through a feature learning perspective. Building on the multi-patch data structure from prior work, our analysis distinguishes between label-dependent feature signals and label-independent noise, a critical aspect overlooked by existing analyses in the DP community. Employing a two-layer CNN with polynomial ReLU activation, we theoretically characterize both feature signal learning and data noise memorization in private training via noisy gradient descent. Our findings reveal that (1) Effective private signal learning requires a higher signal-to-noise ratio (SNR) compared to non-private training, and (2) When data noise memorization occurs in non-private learning, it will also occur in private learning, leading to poor generalization despite small training loss. Our findings highlight the challenges of private learning and prove the benefit of feature enhancement to improve SNR. Experiments on synthetic and real-world datasets also validate our theoretical findings.
November 25, 2025 at 4:54 AM
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Correlated-Sequence Differential Privacy

Yifan Luo, Meng Zhang, Jin Xu, Junting Chen, Jianwei Huang

http://arxiv.org/abs/2511.18025
Correlated-Sequence Differential Privacy Yifan Luo, Meng Zhang, Jin Xu, Junting Chen, Jianwei Huang http://arxiv.org/abs/2511.18025 Data streams collected from multiple sources are rarely independent. Values evolve over time and influence one another across sequences. These correlations improve prediction in healthcare, finance, and smart-city control yet violate the record-independence assumption built into most Differential Privacy (DP) mechanisms. To restore rigorous privacy guarantees without sacrificing utility, we introduce Correlated-Sequence Differential Privacy (CSDP), a framework specifically designed for preserving privacy in correlated sequential data. CSDP addresses two linked challenges: quantifying the extra information an attacker gains from joint temporal and cross-sequence links, and adding just enough noise to hide that information while keeping the data useful. We model multivariate streams as a Coupling Markov Chain, yielding the derived loose leakage bound expressed with a few spectral terms and revealing a counterintuitive result: stronger coupling can actually decrease worst-case leakage by dispersing perturbations across sequences. Guided by these bounds, we build the Freshness-Regulated Adaptive Noise (FRAN) mechanism--combining data aging, correlation-aware sensitivity scaling, and Laplace noise--that runs in linear time. Tests on two-sequence datasets show that CSDP improves the privacy-utility trade-off by approximately 50% over existing correlated-DP methods and by two orders of magnitude compared to the standard DP approach.
November 25, 2025 at 4:54 AM
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Differential privacy with dependent data

Valentin Roth, Marco Avella-Medina

http://arxiv.org/abs/2511.18583
Differential privacy with dependent data Valentin Roth, Marco Avella-Medina http://arxiv.org/abs/2511.18583 Dependent data underlies many statistical studies in the social and health sciences, which often involve sensitive or private information. Differential privacy (DP) and in particular \textit{user-level} DP provide a natural formalization of privacy requirements for processing dependent data where each individual provides multiple observations to the dataset. However, dependence introduced, e.g., through repeated measurements challenges the existing statistical theory under DP-constraints. In \iid{} settings, noisy Winsorized mean estimators have been shown to be minimax optimal for standard (\textit{item-level}) and \textit{user-level} DP estimation of a mean $μ\in \R^d$. Yet, their behavior on potentially dependent observations has not previously been studied. We fill this gap and show that Winsorized mean estimators can also be used under dependence for bounded and unbounded data, and can lead to asymptotic and finite sample guarantees that resemble their \iid{} counterparts under a weak notion of dependence. For this, we formalize dependence via log-Sobolev inequalities on the joint distribution of observations. This enables us to adapt the stable histogram by Karwa and Vadhan (2018) to a non-\iid{} setting, which we then use to estimate the private projection intervals of the Winsorized estimator. The resulting guarantees for our item-level mean estimator extend to \textit{user-level} mean estimation and transfer to the local model via a randomized response histogram. Using the mean estimators as building blocks, we provide extensions to random effects models, longitudinal linear regression and nonparametric regression. Therefore, our work constitutes a first step towards a systematic study of DP for dependent data.
November 25, 2025 at 4:54 AM
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Fairness Meets Privacy: Integrating Differential Privacy and Demographic Parity in Multi-class Classification

Lilian Say, Christophe Denis, Rafael Pinot

http://arxiv.org/abs/2511.18876
Fairness Meets Privacy: Integrating Differential Privacy and Demographic Parity in Multi-class Classification Lilian Say, Christophe Denis, Rafael Pinot http://arxiv.org/abs/2511.18876 The increasing use of machine learning in sensitive applications demands algorithms that simultaneously preserve data privacy and ensure fairness across potentially sensitive sub-populations. While privacy and fairness have each been extensively studied, their joint treatment remains poorly understood. Existing research often frames them as conflicting objectives, with multiple studies suggesting that strong privacy notions such as differential privacy inevitably compromise fairness. In this work, we challenge that perspective by showing that differential privacy can be integrated into a fairness-enhancing pipeline with minimal impact on fairness guarantees. We design a postprocessing algorithm, called DP2DP, that enforces both demographic parity and differential privacy. Our analysis reveals that our algorithm converges towards its demographic parity objective at essentially the same rate (up logarithmic factor) as the best non-private methods from the literature. Experiments on both synthetic and real datasets confirm our theoretical results, showing that the proposed algorithm achieves state-of-the-art accuracy/fairness/privacy trade-offs.
November 25, 2025 at 4:53 AM
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A General Framework for Per-record Differential Privacy

Xinghe Chen, Dajun Sun, Quanqing Xu, Wei Dong

http://arxiv.org/abs/2511.19015
A General Framework for Per-record Differential Privacy Xinghe Chen, Dajun Sun, Quanqing Xu, Wei Dong http://arxiv.org/abs/2511.19015 Differential Privacy (DP) is a widely adopted standard for privacy-preserving data analysis, but it assumes a uniform privacy budget across all records, limiting its applicability when privacy requirements vary with data values. Per-record Differential Privacy (PrDP) addresses this by defining the privacy budget as a function of each record, offering better alignment with real-world needs. However, the dependency between the privacy budget and the data value introduces challenges in protecting the budget's privacy itself. Existing solutions either handle specific privacy functions or adopt relaxed PrDP definitions. A simple workaround is to use the global minimum of the privacy function, but this severely degrades utility, as the minimum is often set extremely low to account for rare records with high privacy needs. In this work, we propose a general and practical framework that enables any standard DP mechanism to support PrDP, with error depending only on the minimal privacy requirement among records actually present in the dataset. Since directly revealing this minimum may leak information, we introduce a core technique called privacy-specified domain partitioning, which ensures accurate estimation without compromising privacy. We also extend our framework to the local DP setting via a novel technique, privacy-specified query augmentation. Using our framework, we present the first PrDP solutions for fundamental tasks such as count, sum, and maximum estimation. Experimental results show that our mechanisms achieve high utility and significantly outperform existing Personalized DP (PDP) methods, which can be viewed as a special case of PrDP with relaxed privacy protection.
November 25, 2025 at 4:53 AM
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Membership Inference Attacks Beyond Overfitting

Mona Khalil, Alberto Blanco-Justicia, Najeeb Jebreel, Josep Domingo-Ferrer

http://arxiv.org/abs/2511.16792
Membership Inference Attacks Beyond Overfitting Mona Khalil, Alberto Blanco-Justicia, Najeeb Jebreel, Josep Domingo-Ferrer http://arxiv.org/abs/2511.16792 Membership inference attacks (MIAs) against machine learning (ML) models aim to determine whether a given data point was part of the model training data. These attacks may pose significant privacy risks to individuals whose sensitive data were used for training, which motivates the use of defenses such as differential privacy, often at the cost of high accuracy losses. MIAs exploit the differences in the behavior of a model when making predictions on samples it has seen during training (members) versus those it has not seen (non-members). Several studies have pointed out that model overfitting is the major factor contributing to these differences in behavior and, consequently, to the success of MIAs. However, the literature also shows that even non-overfitted ML models can leak information about a small subset of their training data. In this paper, we investigate the root causes of membership inference vulnerabilities beyond traditional overfitting concerns and suggest targeted defenses. We empirically analyze the characteristics of the training data samples vulnerable to MIAs in models that are not overfitted (and hence able to generalize). Our findings reveal that these samples are often outliers within their classes (e.g., noisy or hard to classify). We then propose potential defensive strategies to protect these vulnerable samples and enhance the privacy-preserving capabilities of ML models. Our code is available at https://github.com/najeebjebreel/mia_analysis.
November 24, 2025 at 4:53 AM
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Differentially private testing for relevant dependencies in high dimensions

Patrick Bastian, Holger Dette, Martin Dunsche

http://arxiv.org/abs/2511.17167
Differentially private testing for relevant dependencies in high dimensions Patrick Bastian, Holger Dette, Martin Dunsche http://arxiv.org/abs/2511.17167 We investigate the problem of detecting dependencies between the components of a high-dimensional vector. Our approach advances the existing literature in two important respects. First, we consider the problem under privacy constraints. Second, instead of testing whether the coordinates are pairwise independent, we are interested in determining whether certain pairwise associations between the components (such as all pairwise Kendall's $τ$ coefficients) do not exceed a given threshold in absolute value. Considering hypotheses of this form is motivated by the observation that in the high-dimensional regime, it is rare and perhaps impossible to have a null hypothesis that can be modeled exactly by assuming that all pairwise associations are precisely equal to zero. The formulation of the null hypothesis as a composite hypothesis makes the problem of constructing tests already non-standard in the non-private setting. Additionally, under privacy constraints, state of the art procedures rely on permutation approaches that are rendered invalid under a composite null. We propose a novel bootstrap based methodology that is especially powerful in sparse settings, develop theoretical guarantees under mild assumptions and show that the proposed method enjoys good finite sample properties even in the high privacy regime. Additionally, we present applications in medical data that showcase the applicability of our methodology.
November 24, 2025 at 4:53 AM
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🤖 Posts have been a bit erratic lately. This is due to an update to the arXiv API. It should get fixed soon. In the meantime, we've been trying to mitigate the issues. 🤖
November 22, 2025 at 5:32 PM
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Optimal Fairness under Local Differential Privacy

Hrad Ghoukasian, Shahab Asoodeh

http://arxiv.org/abs/2511.16377
Optimal Fairness under Local Differential Privacy Hrad Ghoukasian, Shahab Asoodeh http://arxiv.org/abs/2511.16377 We investigate how to optimally design local differential privacy (LDP) mechanisms that reduce data unfairness and thereby improve fairness in downstream classification. We first derive a closed-form optimal mechanism for binary sensitive attributes and then develop a tractable optimization framework that yields the corresponding optimal mechanism for multi-valued attributes. As a theoretical contribution, we establish that for discrimination-accuracy optimal classifiers, reducing data unfairness necessarily leads to lower classification unfairness, thus providing a direct link between privacy-aware pre-processing and classification fairness. Empirically, we demonstrate that our approach consistently outperforms existing LDP mechanisms in reducing data unfairness across diverse datasets and fairness metrics, while maintaining accuracy close to that of non-private models. Moreover, compared with leading pre-processing and post-processing fairness methods, our mechanism achieves a more favorable accuracy-fairness trade-off while simultaneously preserving the privacy of sensitive attributes. Taken together, these results highlight LDP as a principled and effective pre-processing fairness intervention technique.
November 21, 2025 at 4:55 AM
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Almost Sure Convergence Analysis of Differentially Private Stochastic Gradient Methods

Amartya Mukherjee, Jun Liu

http://arxiv.org/abs/2511.16587
Almost Sure Convergence Analysis of Differentially Private Stochastic Gradient Methods Amartya Mukherjee, Jun Liu http://arxiv.org/abs/2511.16587 Differentially private stochastic gradient descent (DP-SGD) has become the standard algorithm for training machine learning models with rigorous privacy guarantees. Despite its widespread use, the theoretical understanding of its long-run behavior remains limited: existing analyses typically establish convergence in expectation or with high probability, but do not address the almost sure convergence of single trajectories. In this work, we prove that DP-SGD converges almost surely under standard smoothness assumptions, both in nonconvex and strongly convex settings, provided the step sizes satisfy some standard decaying conditions. Our analysis extends to momentum variants such as the stochastic heavy ball (DP-SHB) and Nesterov's accelerated gradient (DP-NAG), where we show that careful energy constructions yield similar guarantees. These results provide stronger theoretical foundations for differentially private optimization and suggest that, despite privacy-induced distortions, the algorithm remains pathwise stable in both convex and nonconvex regimes.
November 21, 2025 at 4:55 AM
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How to Train Private Clinical Language Models: A Comparative Study of Privacy-Preserving Pipelines for ICD-9 Coding

Mathieu Dufour, Andrew Duncan

http://arxiv.org/abs/2511.14936
How to Train Private Clinical Language Models: A Comparative Study of Privacy-Preserving Pipelines for ICD-9 Coding Mathieu Dufour, Andrew Duncan http://arxiv.org/abs/2511.14936 Large language models trained on clinical text risk exposing sensitive patient information, yet differential privacy (DP) methods often severely degrade the diagnostic accuracy needed for deployment. Despite rapid progress in DP optimisation and text generation, it remains unclear which privacy-preserving strategy actually works best for clinical language tasks. We present the first systematic head-to-head comparison of four training pipelines for automated diagnostic coding from hospital discharge summaries. All pipelines use identical 1B-parameter models and matched privacy budgets to predict ICD-9 codes. At moderate and relaxed privacy budgets ($\varepsilon \in \{4, 6\}$), knowledge distillation from DP-trained teachers outperforms both direct DP-SGD and DP-synthetic data training, recovering up to 63\% of the non-private performance whilst maintaining strong empirical privacy (membership-inference AUC $\approx$ 0.5). These findings expose large differences in the privacy-utility trade-off across architectures and identify knowledge distillation as the most practical route to privacy-preserving clinical NLP.
November 20, 2025 at 4:54 AM
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Mutual Information Bounds in the Shuffle Model

Pengcheng Su, Haibo Cheng, Ping Wang

http://arxiv.org/abs/2511.15051
Mutual Information Bounds in the Shuffle Model Pengcheng Su, Haibo Cheng, Ping Wang http://arxiv.org/abs/2511.15051 The shuffle model enhances privacy by anonymizing users' reports through random permutation. This paper presents the first systematic study of the single-message shuffle model from an information-theoretic perspective. We analyze two regimes: the shuffle-only setting, where each user directly submits its message ($Y_i=X_i$), and the shuffle-DP setting, where each user first applies a local $\varepsilon_0$-differentially private mechanism before shuffling ($Y_i=\mathcal{R}(X_i)$). Let $\boldsymbol{Z} = (Y_{σ(i)})_i$ denote the shuffled sequence produced by a uniformly random permutation $σ$, and let $K = σ^{-1}(1)$ represent the position of user 1's message after shuffling. For the shuffle-only setting, we focus on a tractable yet expressive \emph{basic configuration}, where the target user's message follows $Y_1 \sim P$ and the remaining users' messages are i.i.d.\ samples from $Q$, i.e., $Y_2,\dots,Y_n \sim Q$. We derive asymptotic expressions for the mutual information quantities $I(Y_1;\boldsymbol{Z})$ and $I(K;\boldsymbol{Z})$ as $n \to \infty$, and demonstrate how this analytical framework naturally extends to settings with heterogeneous user distributions. For the shuffle-DP setting, we establish information-theoretic upper bounds on total information leakage. When each user applies an $\varepsilon_0$-DP mechanism, the overall leakage satisfies $I(K; \boldsymbol{Z}) \le 2\varepsilon_0$ and $I(X_1; \boldsymbol{Z}\mid (X_i)_{i=2}^n) \le (e^{\varepsilon_0}-1)/(2n) + O(n^{-3/2})$. These results bridge shuffle differential privacy and mutual-information-based privacy.
November 20, 2025 at 4:53 AM
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Privacy-Preserving IoT in Connected Aircraft Cabin

Nilesh Vyas, Benjamin Zhao, Aygün Baltaci, Gustavo de Carvalho Bertoli, Hassan Asghar, Markus Klügel, Gerrit Schramm, Martin Kubisch, Dali Kaafar

http://arxiv.org/abs/2511.15278
Privacy-Preserving IoT in Connected Aircraft Cabin Nilesh Vyas, Benjamin Zhao, Aygün Baltaci, Gustavo de Carvalho Bertoli, Hassan Asghar, Markus Klügel, Gerrit Schramm, Martin Kubisch, Dali Kaafar http://arxiv.org/abs/2511.15278 The proliferation of IoT devices in shared, multi-vendor environments like the modern aircraft cabin creates a fundamental conflict between the promise of data collaboration and the risks to passenger privacy, vendor intellectual property (IP), and regulatory compliance. While emerging standards like the Cabin Secure Media-Independent Messaging (CSMIM) protocol provide a secure communication backbone, they do not resolve data governance challenges at the application layer, leaving a privacy gap that impedes trust. This paper proposes and evaluates a framework that closes this gap by integrating a configurable layer of Privacy-Enhancing Technologies (PETs) atop a CSMIM-like architecture. We conduct a rigorous, empirical analysis of two pragmatic PETs: Differential Privacy (DP) for statistical sharing, and an additive secret sharing scheme (ASS) for data obfuscation. Using a high-fidelity testbed with resource-constrained hardware, we quantify the trade-offs between data privacy, utility, and computing performance. Our results demonstrate that the computational overhead of PETs is often negligible compared to inherent network and protocol latencies. We prove that architectural choices, such as on-device versus virtualized processing, have a far greater impact on end-to-end latency and computational performance than the PETs themselves. The findings provide a practical roadmap for system architects to select and configure appropriate PETs, enabling the design of trustworthy collaborative IoT ecosystems in avionics and other critical domains.
November 20, 2025 at 4:53 AM
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Rényi Differential Privacy for Heavy-Tailed SDEs via Fractional Poincaré Inequalities

Benjamin Dupuis, Mert Gürbüzbalaban, Umut Şimşekli, Jian Wang, Sinan Yildirim, Lingjiong Zhu

http://arxiv.org/abs/2511.15634
Rényi Differential Privacy for Heavy-Tailed SDEs via Fractional Poincaré Inequalities Benjamin Dupuis, Mert Gürbüzbalaban, Umut Şimşekli, Jian Wang, Sinan Yildirim, Lingjiong Zhu http://arxiv.org/abs/2511.15634 Characterizing the differential privacy (DP) of learning algorithms has become a major challenge in recent years. In parallel, many studies suggested investigating the behavior of stochastic gradient descent (SGD) with heavy-tailed noise, both as a model for modern deep learning models and to improve their performance. However, most DP bounds focus on light-tailed noise, where satisfactory guarantees have been obtained but the proposed techniques do not directly extend to the heavy-tailed setting. Recently, the first DP guarantees for heavy-tailed SGD were obtained. These results provide $(0,δ)$-DP guarantees without requiring gradient clipping. Despite casting new light on the link between DP and heavy-tailed algorithms, these results have a strong dependence on the number of parameters and cannot be extended to other DP notions like the well-established Rényi differential privacy (RDP). In this work, we propose to address these limitations by deriving the first RDP guarantees for heavy-tailed SDEs, as well as their discretized counterparts. Our framework is based on new Rényi flow computations and the use of well-established fractional Poincaré inequalities. Under the assumption that such inequalities are satisfied, we obtain DP guarantees that have a much weaker dependence on the dimension compared to prior art.
November 20, 2025 at 4:53 AM
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Beyond One-Size-Fits-All: Neural Networks for Differentially Private Tabular Data Synthesis

Kai Chen, Chen Gong, Tianhao Wang

http://arxiv.org/abs/2511.13893
Beyond One-Size-Fits-All: Neural Networks for Differentially Private Tabular Data Synthesis Kai Chen, Chen Gong, Tianhao Wang http://arxiv.org/abs/2511.13893 In differentially private (DP) tabular data synthesis, the consensus is that statistical models are better than neural network (NN)-based methods. However, we argue that this conclusion is incomplete and overlooks the challenge of densely correlated datasets, where intricate dependencies can overwhelm statistical models. In such complex scenarios, neural networks are more suitable due to their capacity to fit complex distributions by learning directly from samples. Despite this potential, existing NN-based algorithms still suffer from significant limitations. We therefore propose MargNet, incorporating successful algorithmic designs of statistical models into neural networks. MargNet applies an adaptive marginal selection strategy and trains the neural networks to generate data that conforms to the selected marginals. On sparsely correlated datasets, our approach achieves utility close to the best statistical method while offering an average 7$\times$ speedup over it. More importantly, on densely correlated datasets, MargNet establishes a new state-of-the-art, reducing fidelity error by up to 26\% compared to the previous best. We release our code on GitHub.\footnote{https://github.com/KaiChen9909/margnet}
November 19, 2025 at 4:53 AM
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On the Gradient Complexity of Private Optimization with Private Oracles

Michael Menart, Aleksandar Nikolov

http://arxiv.org/abs/2511.13999
On the Gradient Complexity of Private Optimization with Private Oracles Michael Menart, Aleksandar Nikolov http://arxiv.org/abs/2511.13999 We study the running time, in terms of first order oracle queries, of differentially private empirical/population risk minimization of Lipschitz convex losses. We first consider the setting where the loss is non-smooth and the optimizer interacts with a private proxy oracle, which sends only private messages about a minibatch of gradients. In this setting, we show that expected running time $Ω(\min\{\frac{\sqrt{d}}{α^2}, \frac{d}{\log(1/α)}\})$ is necessary to achieve $α$ excess risk on problems of dimension $d$ when $d \geq 1/α^2$. Upper bounds via DP-SGD show these results are tight when $d>\tildeΩ(1/α^4)$. We further show our lower bound can be strengthened to $Ω(\min\{\frac{d}{\bar{m}α^2}, \frac{d}{\log(1/α)} \})$ for algorithms which use minibatches of size at most $\bar{m} < \sqrt{d}$. We next consider smooth losses, where we relax the private oracle assumption and give lower bounds under only the condition that the optimizer is private. Here, we lower bound the expected number of first order oracle calls by $\tildeΩ\big(\frac{\sqrt{d}}α + \min\{\frac{1}{α^2}, n\}\big)$, where $n$ is the size of the dataset. Modifications to existing algorithms show this bound is nearly tight. Compared to non-private lower bounds, our results show that differentially private optimizers pay a dimension dependent runtime penalty. Finally, as a natural extension of our proof technique, we show lower bounds in the non-smooth setting for optimizers interacting with information limited oracles. Specifically, if the proxy oracle transmits at most $Γ$-bits of information about the gradients in the minibatch, then $Ω\big(\min\{\frac{d}{α^2Γ}, \frac{d}{\log(1/α)}\}\big)$ oracle calls are needed. This result shows fundamental limitations of gradient quantization techniques in optimization.
November 19, 2025 at 4:53 AM
dppapers.bsky.social
Observational Auditing of Label Privacy

Iden Kalemaj, Luca Melis, Maxime Boucher, Ilya Mironov, Saeed Mahloujifar

http://arxiv.org/abs/2511.14084
Observational Auditing of Label Privacy Iden Kalemaj, Luca Melis, Maxime Boucher, Ilya Mironov, Saeed Mahloujifar http://arxiv.org/abs/2511.14084 Differential privacy (DP) auditing is essential for evaluating privacy guarantees in machine learning systems. Existing auditing methods, however, pose a significant challenge for large-scale systems since they require modifying the training dataset -- for instance, by injecting out-of-distribution canaries or removing samples from training. Such interventions on the training data pipeline are resource-intensive and involve considerable engineering overhead. We introduce a novel observational auditing framework that leverages the inherent randomness of data distributions, enabling privacy evaluation without altering the original dataset. Our approach extends privacy auditing beyond traditional membership inference to protected attributes, with labels as a special case, addressing a key gap in existing techniques. We provide theoretical foundations for our method and perform experiments on Criteo and CIFAR-10 datasets that demonstrate its effectiveness in auditing label privacy guarantees. This work opens new avenues for practical privacy auditing in large-scale production environments.
November 19, 2025 at 4:53 AM
dppapers.bsky.social
Whistledown: Combining User-Level Privacy with Conversational Coherence in LLMs

Chelsea McMurray, Hayder Tirmazi

http://arxiv.org/abs/2511.13319
Whistledown: Combining User-Level Privacy with Conversational Coherence in LLMs Chelsea McMurray, Hayder Tirmazi http://arxiv.org/abs/2511.13319 Users increasingly rely on large language models (LLMs) for personal, emotionally charged, and socially sensitive conversations. However, prompts sent to cloud-hosted models can contain personally identifiable information (PII) that users do not want logged, retained, or leaked. We observe this to be especially acute when users discuss friends, coworkers, or adversaries, i.e., when they spill the tea. Enterprises face the same challenge when they want to use LLMs for internal communication and decision-making. In this whitepaper, we present Whistledown, a best-effort privacy layer that modifies prompts before they are sent to the LLM. Whistledown combines pseudonymization and $ε$-local differential privacy ($ε$-LDP) with transformation caching to provide best-effort privacy protection without sacrificing conversational utility. Whistledown is designed to have low compute and memory overhead, allowing it to be deployed directly on a client's device in the case of individual users. For enterprise users, Whistledown is deployed centrally within a zero-trust gateway that runs on an enterprise's trusted infrastructure. Whistledown requires no changes to the existing APIs of popular LLM providers.
November 18, 2025 at 4:53 AM