AI 中文总结
trasgoDP是开源Python框架，用于在局部差分隐私下发布表格微数据等。它能探索隐私-效用权衡，实现多种机制和度量，为评估相关数据发布方法提供可重现的开源基线。
AI 中文摘要
trasgoDP是一个模块化、开源且易于使用的Python框架，旨在在标准数据科学工作流程中安装和集成，用于在ε-局部差分隐私保证下发布表格微数据，以及在地理不可区分性假设下发布位置数据。该软件能系统地探索跨多种机制、数据类型和ε值的隐私-效用权衡。虽然差分隐私在聚合数据方面已被广泛研究，但在可重复使用的软件工具方面，其在按行微数据发布中的应用仍未得到充分利用，在度量隐私和基于位置的数据方面差距更为明显。trasgoDP实现了用于数值和分类属性的局部差分隐私机制（拉普拉斯、高斯、指数和随机响应）、用于位置数据的地理不可区分性机制以及一组效用度量，包括一种新颖的相关损失度量，以量化作为分配隐私预算函数的信息损失。这项工作的目标是为研究社区提供一个可重现的开源基线，用于在正式的局部差分隐私保证下评估表格和基于位置的数据发布方法。
英文摘要
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.
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