Privacy Mechanism Design based on Empirical Distributions

• URL: http://arxiv.org/abs/2509.22428v1
• Date: Fri, 26 Sep 2025 14:46:59 GMT
• Title: Privacy Mechanism Design based on Empirical Distributions
• Authors: Leonhard Grosse, Sara Saeidian, Mikael Skoglund, Tobias J. Oechtering,
• Abstract summary: Pointwise maximal leakage (PML) is a per-outcome privacy measure based on threat models from quantitative information flow.<n>We propose a framework for PML privacy assessment and mechanism design with empirical estimates of this data-generating distribution.
• Score: 36.33156483258328
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Pointwise maximal leakage (PML) is a per-outcome privacy measure based on threat models from quantitative information flow. Privacy guarantees with PML rely on knowledge about the distribution that generated the private data. In this work, we propose a framework for PML privacy assessment and mechanism design with empirical estimates of this data-generating distribution. By extending the PML framework to consider sets of data-generating distributions, we arrive at bounds on the worst-case leakage within a given set. We use these bounds alongside large-deviation bounds from the literature to provide a method for obtaining distribution-independent $(\varepsilon,\delta)$-PML guarantees when the data-generating distribution is estimated from available data samples. We provide an optimal binary mechanism, and show that mechanism design with this type of uncertainty about the data-generating distribution reduces to a linearly constrained convex program. Further, we show that optimal mechanisms designed for a distribution estimate can be used. Finally, we apply these tools to leakage assessment of the Laplace mechanism and the Gaussian mechanism for binary private data, and numerically show that the presented approach to mechanism design can yield significant utility increase compared to local differential privacy, while retaining similar privacy guarantees.

Related papers

• Fundamental Limit of Discrete Distribution Estimation under Utility-Optimized Local Differential Privacy [14.980778567896593]
  We study the problem of discrete distribution estimation under utility-optimized local differential privacy (ULDP)<n>For the achievability, we propose a class of utility-optimized block design (uBD) schemes, obtained as non-preserving modifications of the block design mechanism known to be optimal under standard LDP constraints.<n>These results provide a tight characterization of the estimation accuracy achievable under ULDP and reveal new insights into the structure of optimal mechanisms for privacy-trivial statistical inference.
  arXiv  Detail & Related papers  (2025-09-29T01:41:36Z)
• Differentially Private Distribution Release of Gaussian Mixture Models via KL-Divergence Minimization [5.615206798152645]
  We introduce a DP mechanism that adds carefully calibrated random perturbations to the GMM parameters.<n>Our approach achieves strong privacy guarantees while maintaining high utility.
  arXiv  Detail & Related papers  (2025-06-04T00:40:24Z)
• Verifiable Exponential Mechanism for Median Estimation [4.083860866484599]
  We propose the first verifiable implementation of the exponential mechanism using zk-SNARKs.<n>As a concrete application, we present the first verifiable differentially private (DP) median estimation scheme.
  arXiv  Detail & Related papers  (2025-05-22T05:31:22Z)
• DMM: Distributed Matrix Mechanism for Differentially-Private Federated Learning Based on Constant-Overhead Linear Secret Resharing [51.336015600778396]
  We introduce the distributed matrix mechanism to achieve the best-of-both-worlds; better privacy of distributed DP and better utility from the matrix mechanism.<n>We accomplish this using a novel cryptographic protocol that securely transfers sensitive values across client committees of different training iterations with constant communication overhead.
  arXiv  Detail & Related papers  (2024-10-21T16:25:14Z)
• CorBin-FL: A Differentially Private Federated Learning Mechanism using Common Randomness [6.881974834597426]
  Federated learning (FL) has emerged as a promising framework for distributed machine learning. We introduce CorBin-FL, a privacy mechanism that uses correlated binary quantization to achieve differential privacy. We also propose AugCorBin-FL, an extension that, in addition to PLDP, user-level and sample-level central differential privacy guarantees.
  arXiv  Detail & Related papers  (2024-09-20T00:23:44Z)
• Privacy Amplification for the Gaussian Mechanism via Bounded Support [64.86780616066575]
  Data-dependent privacy accounting frameworks such as per-instance differential privacy (pDP) and Fisher information loss (FIL) confer fine-grained privacy guarantees for individuals in a fixed training dataset. We propose simple modifications of the Gaussian mechanism with bounded support, showing that they amplify privacy guarantees under data-dependent accounting.
  arXiv  Detail & Related papers  (2024-03-07T21:22:07Z)
• Probing the Transition to Dataset-Level Privacy in ML Models Using an Output-Specific and Data-Resolved Privacy Profile [23.05994842923702]
  We study a privacy metric that quantifies the extent to which a model trained on a dataset using a Differential Privacy mechanism is covered" by each of the distributions resulting from training on neighboring datasets. We show that the privacy profile can be used to probe an observed transition to indistinguishability that takes place in the neighboring distributions as $epsilon$ decreases.
  arXiv  Detail & Related papers  (2023-06-27T20:39:07Z)
• Breaking the Communication-Privacy-Accuracy Tradeoff with $f$-Differential Privacy [51.11280118806893]
  We consider a federated data analytics problem in which a server coordinates the collaborative data analysis of multiple users with privacy concerns and limited communication capability. We study the local differential privacy guarantees of discrete-valued mechanisms with finite output space through the lens of $f$-differential privacy (DP) More specifically, we advance the existing literature by deriving tight $f$-DP guarantees for a variety of discrete-valued mechanisms.
  arXiv  Detail & Related papers  (2023-02-19T16:58:53Z)
• DP2-Pub: Differentially Private High-Dimensional Data Publication with Invariant Post Randomization [58.155151571362914]
  We propose a differentially private high-dimensional data publication mechanism (DP2-Pub) that runs in two phases. splitting attributes into several low-dimensional clusters with high intra-cluster cohesion and low inter-cluster coupling helps obtain a reasonable privacy budget. We also extend our DP2-Pub mechanism to the scenario with a semi-honest server which satisfies local differential privacy.
  arXiv  Detail & Related papers  (2022-08-24T17:52:43Z)
• The Poisson binomial mechanism for secure and private federated learning [19.399122892615573]
  We introduce a discrete differential privacy mechanism for distributed mean estimation (DME) with applications to federated learning and analytics. We provide a tight analysis of its privacy guarantees, showing that it achieves the same privacy-accuracy trade-offs as the continuous Gaussian mechanism.
  arXiv  Detail & Related papers  (2022-07-09T05:46:28Z)
• Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift [100.52588638477862]
  We develop an approximate Bayesian inference scheme based on posterior regularisation. We demonstrate the utility of our method in the context of transferring prognostic models of prostate cancer across globally diverse populations.
  arXiv  Detail & Related papers  (2020-06-26T13:50:19Z)

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.