trasgoDP: An open source framework for releasing noised tabular microdata under local differential privacy
Published in SoftwareX, 2026
Abstract 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.
Recommended citation: Sáinz-Pardo Díaz, J., & López García, Á. (2026). trasgoDP: An open source framework for releasing noised tabular microdata under local differential privacy. SoftwareX, 36, https://doi.org/10.1016/j.softx.2026.103171.
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