Differential Privacy
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Related Articles from SNS
Optimal conversion from R\'enyi Differential Privacy to $f$-Differential Privacy
arXiv:2602.04562v3 Announce Type: replace Abstract: We prove the conjecture stated in Appendix F.3 of \citet{zhu2022optimalaccountingdifferentialprivacy}: among all conversion rules that map a R\'enyi Differential Privacy (RDP) profile $\tau \mapsto \rho(\tau)$ to a valid hypothesis-testing trade-off $f$, the rule based on the intersection of single-order RDP privacy regions is optimal. This optimality holds simultaneously for all valid RDP profiles and for all Type I error levels $\alpha$....
Membership Reference Attack against Laplace Mechanism of Differential Privacy
arXiv:2409.08784v4 Announce Type: replace Abstract: The differential privacy is a widely accepted conception of privacy protection and the Laplace mechanism is a famous instance of differential privacy mechanisms to deal with numerical data. In this paper, we point out that the differential privacy does not take liner property of queries into account, resulting in information leakage. In order to show the information leakage, we construct a membership reference attacks against the Laplace...
A Unified Framework for Adversary-Aware Differential Privacy Bounds
arXiv:2507.08158v2 Announce Type: replace Abstract: Differential Privacy (DP) bounds the privacy leakage of a mechanism against worst-case membership inference, but the precise tradeoff between complex adversarial models and DP protections remains poorly understood. In this paper, we present a unified framework that generalizes the patchwork of existing bounds across membership inference, attribute inference, and data reconstruction attacks. Crucially, our framework is the first to evaluate...
On Choosing the $\mu$ Parameter in Gaussian Differential Privacy
Announce Type: new Abstract: Recent work argues for using Gaussian differential privacy (GDP) to report the privacy guarantees in privacy-preserving machine learning. We provide principled mappings from pure-DP $\varepsilon$ to GDP $\mu$ by matching the worst-case success of a strong-adversary membership inference attack in terms of three metrics: multiplicative advantage at fixed FPR, precision at fixed recall, and the standard privacy profile.
Accuracy-First R\'enyi Differential Privacy and Post-Processing Immunity
arXiv:2509.22213v2 Announce Type: replace Abstract: The accuracy-first perspective of differential privacy addresses an important shortcoming by allowing a data analyst to adaptively adjust the quantitative privacy bound instead of sticking to a predetermined bound. Existing works on the accuracy-first perspective have neglected an important property of differential privacy known as post-processing immunity, which ensures that an adversary is not able to weaken the privacy guarantee by...
Scalable and Private Federated Learning Using Distributed Differential Privacy and Secure Aggregation
arXiv:2604.07125v2 Announce Type: replace Abstract: This article presents DDP-SA, a scalable privacy-preserving federated learning framework that jointly leverages client-side local differential privacy (LDP) and full-threshold additive secret sharing (ASS) for secure aggregation. Unlike existing methods that rely solely on differential privacy or on secure multi-party computation (MPC), DDP-SA integrates both techniques to deliver stronger end-to-end privacy guarantees while remaining...
Publishing Below-Threshold Triangle Counts under Local Weight Differential Privacy
arXiv:2601.01710v3 Announce Type: replace Abstract: We propose an algorithm for counting below-threshold triangles in weighted graphs under local weight differential privacy. While prior work has largely focused on unweighted graphs, edge weights are intrinsic to many real-world networks. We consider the setting in which the graph topology is publicly known and privacy is required only for the contribution of an individual to incident edge weights, capturing practical scenarios such as road...
Fully Oblivious Differential Privacy for Frequency Estimation in the Augmented Shuffle Model with Trusted Processors
Announce Type: new Abstract: In the shuffle model of DP (Differential Privacy), a shuffler randomly permutes users' data to achieve high accuracy and privacy. Recent studies show that most existing shuffle protocols are vulnerable to collusion attacks by the data collector and users. They address this issue by introducing the augmented shuffle model that incorporates random sampling and dummy data addition into the shuffler.
Asymptotic Optimality of the High-Dimensional Gaussian Mechanism and Improved Low-Dimensional Mechanisms for Differential Privacy
arXiv:2606.08681v1 Announce Type: new Abstract: The additive noise mechanism is a foundational tool for differential privacy (DP) of $T$-dimensional real-valued vector queries. The Gaussian mechanism, utilizing Gaussian noise, is the mostly widely used such mechanism, due to its simplicity and strong privacy guarantees. In this work, we provide justification for this choice, showing that as the dimension $T\to\infty$, no additive-noise mechanism can asymptotically improve on the Gaussian...
Local Differential Privacy with Correlated Noise Achieves Central-DP Optimal Cost
arXiv:2605.30476v1 Announce Type: new Abstract: We study privately estimating the sum of $n$ user-held values in the presence of an honest-but-curious server. This motivates requiring privacy not only at data release but also throughout server-side computation. We therefore adopt the local (pure) differential privacy model, in which each user transmits a noise-perturbed value.