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

2026-07-24

AI Summary

This work addresses the lack of unified support in existing open-source tools for evaluating privacy-utility trade-offs under local differential privacy (LDP) or geo-indistinguishability, particularly for row-level microdata and spatial data. To bridge this gap, the authors propose a modular and reproducible Python framework that, for the first time, integrates standard LDP mechanisms—including Laplace, Gaussian, randomized response, and the exponential mechanism—with geo-indistinguishability techniques. The framework supports privacy-preserving release of numerical, categorical, and location data, and introduces a novel correlation-based loss metric alongside multiple utility measures. By providing a standardized benchmark, it enables comprehensive assessment of information loss and privacy guarantees across diverse data types, privacy budgets, and perturbation mechanisms.

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.

Problem

• local differential privacy
• tabular microdata
• geo-indistinguishability
• privacy-utility trade-off
• location data

Innovation

• local differential privacy
• geo-indistinguishability
• tabular microdata
• privacy-utility trade-off
• correlation-loss