trasgoDP: An open source framework for releasing noised tabular microdata under local differential privacy

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

This paper presents trasgoDP , a modular Python framework for releasing tabular microdata under record-level local differential privacy guarantees. Designed for integration within standard data science workflows, the software enables systematic exploration of privacy-utility trade-offs across multiple mechanisms, data types, and privacy budgets. Unlike existing software products focused on applying differential privacy to aggregated data, trasgoDP aims to support row-wise microdata and location data release with formal local differential privacy and metric-privacy guarantees. The framework enables reproducible experimentation, generation of noisy datasets, and extensible integration into reproducible privacy-preserving data publishing and benchmarking pipelines for data-based research.