Robust Single-message Shuffle Differential Privacy Protocol for Accurate Distribution Estimation

• URL: http://arxiv.org/abs/2603.05073v1
• Date: Thu, 05 Mar 2026 11:40:26 GMT
• Title: Robust Single-message Shuffle Differential Privacy Protocol for Accurate Distribution Estimation
• Authors: Xiaoguang Li, Hanyi Wang, Yaowei Huang, Jungang Yang, Qingqing Ye, Haonan Yan, Ke Pan, Zhe Sun, Hui Li,
• Abstract summary: We study the distribution estimation under pure shuffle model, which is a prevalent shuffle-DP framework without strong security assumptions.<n>We propose a novel single-message textitadaptive shuffler-based piecewise (ASP) protocol with high utility and robustness.
• Score: 29.22457447003792
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Shuffler-based differential privacy (shuffle-DP) is a privacy paradigm providing high utility by involving a shuffler to permute noisy report from users. Existing shuffle-DP protocols mainly focus on the design of shuffler-based categorical frequency oracle (SCFO) for frequency estimation on categorical data. However, numerical data is a more prevalent type and many real-world applications depend on the estimation of data distribution with ordinal nature. In this paper, we study the distribution estimation under pure shuffle model, which is a prevalent shuffle-DP framework without strong security assumptions. We initially attempt to transplant existing SCFOs and the naïve distribution recovery technique to this task, and demonstrate that these baseline protocols cannot simultaneously achieve outstanding performance in three metrics: 1) utility, 2) message complexity; and 3) robustness to data poisoning attacks. Therefore, we further propose a novel single-message \textit{adaptive shuffler-based piecewise} (ASP) protocol with high utility and robustness. In ASP, we first develop a randomizer by parameter optimization using our proposed tighter bound of mutual information. We also design an \textit{Expectation Maximization with Adaptive Smoothing} (EMAS) algorithm to accurately recover distribution with enhanced robustness. To quantify robustness, we propose a new evaluation framework to examine robustness under different attack targets, enabling us to comprehensively understand the protocol resilience under various adversarial scenarios. Extensive experiments demonstrate that ASP outperforms baseline protocols in all three metrics. Especially under small $ε$ values, ASP achieves an order of magnitude improvement in utility with minimal message complexity, and exhibits over threefold robustness compared to baseline methods.

Related papers

• Benchmarking Few-shot Transferability of Pre-trained Models with Improved Evaluation Protocols [123.73663884421272]
  Few-shot transfer has been revolutionized by stronger pre-trained models and improved adaptation algorithms.<n>We establish FEWTRANS, a comprehensive benchmark containing 10 diverse datasets.<n>By releasing FEWTRANS, we aim to provide a rigorous "ruler" to streamline reproducible advances in few-shot transfer learning research.
  arXiv  Detail & Related papers  (2026-02-28T05:41:57Z)
• Fidelity-Aware Recommendation Explanations via Stochastic Path Integration [14.312396553281316]
  Explanation fidelity, which measures how accurately an explanation reflects a model's true reasoning, remains critically underexplored in recommender systems.<n>We introduce SPINRec, a model-agnostic approach that adapts path-integration techniques to the sparse and implicit nature of recommendation data.<n>SPINRec consistently outperforms all baselines, establishing a new benchmark for faithful explainability in recommendation.
  arXiv  Detail & Related papers  (2025-11-22T12:59:04Z)
• Learning Discrete Bayesian Networks with Hierarchical Dirichlet Shrinkage [52.914168158222765]
  We detail a comprehensive Bayesian framework for learning DBNs.<n>We give a novel Markov chain Monte Carlo (MCMC) algorithm utilizing parallel Langevin proposals to generate exact posterior samples.<n>We apply our methodology to uncover prognostic network structure from primary breast cancer samples.
  arXiv  Detail & Related papers  (2025-09-16T17:24:35Z)
• Augmented Shuffle Protocols for Accurate and Robust Frequency Estimation under Differential Privacy [4.527947047128005]
  We propose three concrete protocols providing DP and robustness against the two attacks.<n>Our first protocol generates the number of dummy values for each item from a binomial distribution.<n>Our second protocol significantly improves the utility of our first protocol by introducing a novel dummy-count distribution.
  arXiv  Detail & Related papers  (2025-04-10T01:06:05Z)
• Lightweight Federated Learning with Differential Privacy and Straggler Resilience [19.94124499453864]
  Federated learning (FL) enables collaborative model training through model parameter exchanges instead of raw data.<n>To avoid potential inference attacks from exchanged parameters, differential privacy (DP) offers rigorous guarantee against various attacks.<n>We propose LightDP-FL, a novel lightweight scheme that ensures provable DP against untrusted peers and server.
  arXiv  Detail & Related papers  (2024-12-09T00:54:00Z)
• Benchmarking Secure Sampling Protocols for Differential Privacy [3.0325535716232404]
  Two well-known models of Differential Privacy (DP) are the central model and the local model. Recently, many studies have proposed to achieve DP with Secure Multi-party Computation (MPC) in distributed settings.
  arXiv  Detail & Related papers  (2024-09-16T19:04:47Z)
• PriRoAgg: Achieving Robust Model Aggregation with Minimum Privacy Leakage for Federated Learning [49.916365792036636]
  Federated learning (FL) has recently gained significant momentum due to its potential to leverage large-scale distributed user data.<n>The transmitted model updates can potentially leak sensitive user information, and the lack of central control of the local training process leaves the global model susceptible to malicious manipulations on model updates.<n>We develop a general framework PriRoAgg, utilizing Lagrange coded computing and distributed zero-knowledge proof, to execute a wide range of robust aggregation algorithms while satisfying aggregated privacy.
  arXiv  Detail & Related papers  (2024-07-12T03:18:08Z)
• Privacy Amplification via Shuffled Check-Ins [2.3333090554192615]
  We study a protocol for distributed computation called shuffled check-in. It achieves strong privacy guarantees without requiring any further trust assumptions beyond a trusted shuffler. We show that shuffled check-in achieves tight privacy guarantees through privacy amplification.
  arXiv  Detail & Related papers  (2022-06-07T09:55:15Z)
• Distributionally Robust Models with Parametric Likelihood Ratios [123.05074253513935]
  Three simple ideas allow us to train models with DRO using a broader class of parametric likelihood ratios. We find that models trained with the resulting parametric adversaries are consistently more robust to subpopulation shifts when compared to other DRO approaches.
  arXiv  Detail & Related papers  (2022-04-13T12:43:12Z)
• Scalable Uncertainty Quantification for Deep Operator Networks using Randomized Priors [14.169588600819546]
  We present a simple and effective approach for posterior uncertainty quantification in deep operator networks (DeepONets) We adopt a frequentist approach based on randomized prior ensembles, and put forth an efficient vectorized implementation for fast parallel inference on accelerated hardware.
  arXiv  Detail & Related papers  (2022-03-06T20:48:16Z)
• Noise-Resistant Deep Metric Learning with Probabilistic Instance Filtering [59.286567680389766]
  Noisy labels are commonly found in real-world data, which cause performance degradation of deep neural networks. We propose Probabilistic Ranking-based Instance Selection with Memory (PRISM) approach for DML. PRISM calculates the probability of a label being clean, and filters out potentially noisy samples.
  arXiv  Detail & Related papers  (2021-08-03T12:15:25Z)
• Improved, Deterministic Smoothing for L1 Certified Robustness [119.86676998327864]
  We propose a non-additive and deterministic smoothing method, Deterministic Smoothing with Splitting Noise (DSSN) In contrast to uniform additive smoothing, the SSN certification does not require the random noise components used to be independent. This is the first work to provide deterministic "randomized smoothing" for a norm-based adversarial threat model.
  arXiv  Detail & Related papers  (2021-03-17T21:49:53Z)
• Meta-Learned Confidence for Few-shot Learning [60.6086305523402]
  A popular transductive inference technique for few-shot metric-based approaches, is to update the prototype of each class with the mean of the most confident query examples. We propose to meta-learn the confidence for each query sample, to assign optimal weights to unlabeled queries. We validate our few-shot learning model with meta-learned confidence on four benchmark datasets.
  arXiv  Detail & Related papers  (2020-02-27T10:22:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.