trasgoDP: An Open Source Framework for Releasing Noised Tabular Microdata under Local Differential Privacy

• URL: http://arxiv.org/abs/2607.22230v1
• Date: Fri, 24 Jul 2026 11:56:09 GMT
• Title: trasgoDP: An Open Source Framework for Releasing Noised Tabular Microdata under Local Differential Privacy
• Abstract summary: trasgoDP is a Python framework for releasing microdata under -local differential privacy guarantees.<n>TrasgoDP implements local-DP mechanisms for numerical and categorical attributes, a geo-indistinguishability mechanism for location data, and a set of utility metrics.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: trasgoDP is a modular, open-source, and easy-to-use Python framework for releasing tabular microdata under ε-local differential privacy guarantees, as well as location data under geo-indistinguishability assumptions, designed to be installed and integrated within standard data science workflows. The software enables systematic exploration of privacy-utility trade-offs across multiple mechanisms, data types, and ε values. While differential privacy has been extensively studied for aggregate data, its application to row-wise microdata release remains underexploited in terms of reusable software tools, a gap that is even more pronounced in the case of metric privacy and location-based data. trasgoDP implements local-DP mechanisms for numerical and categorical attributes (Laplace, Gaussian, Exponential, and Randomized Response), a geo-indistinguishability mechanism for location data, and a set of utility metrics, including a novel correlation-loss measure, to quantify information loss as a function of the allocated privacy budget. The objective of this work is to provide the research community with a reproducible, open-source baseline for evaluating tabular and location-based data publication methodologies under formal local differential privacy guarantees.

Related papers

• Towards Personalized Differentially Private Learning for Decentralized Local Graphs [62.244412935520025]
  Local Differential Privacy (LDP) has emerged as a promising solution for privacy-preserving data collection without relying on trusted servers.<n>Existing LDP-based graph learning methods typically assume uniform privacy requirements across users.<n>We propose PPGNN, a personalized differentially private framework for decentralized graph data.
  arXiv  Detail & Related papers  (2026-07-06T08:07:48Z)
• Phantoms and Disclosures: a Causal Framework for Auditing Synthetic Data [56.86147283213652]
  We present a customizable empirical auditing framework designed to detect and explain data disclosures.<n>Our framework introduces a mechanism to distinguish between "true disclosures"-where the system directly reproduces a user's information-and "phantom disclosures"-where the system incidentally generates a user's data.
  arXiv  Detail & Related papers  (2026-06-15T16:54:02Z)
• Differentially Private Manifold Denoising [5.0794787043927725]
  We introduce a differentially private manifold denoising framework that allows users to exploit sensitive reference datasets to correct noisy, non-private query points.<n>The framework performs rigorous privacy accounting across iterations and queries using $(varepsilon,)$-differential privacy (DP)<n> Practically, this framework brings differential privacy to manifold methods, retaining sufficient geometric signal for downstream tasks such as embedding, clustering, and visualization.
  arXiv  Detail & Related papers  (2026-04-01T14:13:59Z)
• Unified Locational Differential Privacy Framework [0.0]
  We present a unified locational differential privacy (DP) framework to enable private aggregation of various data types over geographical regions. Results demonstrate the utility of our framework in providing formal DP guarantees while enabling geographical data analysis.
  arXiv  Detail & Related papers  (2024-05-06T23:33:52Z)
• Local Differential Privacy in Graph Neural Networks: a Reconstruction Approach [17.000441871334683]
  We propose a learning framework that can provide node privacy at the user level, while incurring low utility loss. We focus on a decentralized notion of Differential Privacy, namely Local Differential Privacy. We develop reconstruction methods to approximate features and labels from perturbed data.
  arXiv  Detail & Related papers  (2023-09-15T17:35:51Z)
• 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)
• GenSyn: A Multi-stage Framework for Generating Synthetic Microdata using Macro Data Sources [21.32471030724983]
  Individual-level data (microdata) that characterizes a population is essential for studying many real-world problems. In this study, we examine synthetic data generation as a tool to extrapolate difficult-to-obtain high-resolution data.
  arXiv  Detail & Related papers  (2022-12-08T01:22:12Z)
• 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)
• Releasing survey microdata with exact cluster locations and additional privacy safeguards [77.34726150561087]
  We propose an alternative microdata dissemination strategy that leverages the utility of the original microdata with additional privacy safeguards. Our strategy reduces the respondents' re-identification risk for any number of disclosed attributes by 60-80% even under re-identification attempts.
  arXiv  Detail & Related papers  (2022-05-24T19:37:11Z)
• Graph-Homomorphic Perturbations for Private Decentralized Learning [64.26238893241322]
  Local exchange of estimates allows inference of data based on private data. perturbations chosen independently at every agent, resulting in a significant performance loss. We propose an alternative scheme, which constructs perturbations according to a particular nullspace condition, allowing them to be invisible.
  arXiv  Detail & Related papers  (2020-10-23T10:35:35Z)

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.