# An Open Source Python Library for Anonymizing Sensitive Data

arXiv: https://arxiv.org/abs/2408.10766 · HTML: https://mlsanity.com/paper/2408.10766/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2024.

## Built on by (most cited first, 4 of 4)

- [Small Language Models are the Future of Agentic AI](https://mlsanity.com/paper/2506.02153/) (2025)
- [Fair Play for Individuals, Foul Play for Groups? Auditing Anonymization's Impact on ML Fairness](https://mlsanity.com/paper/2505.07985/) (2025)
- [Aegis: A Correlation-Based Data Masking Advisor for Data Sharing Ecosystems](https://mlsanity.com/paper/2510.10810/) (2025)
- [Privacy Preserving Machine Learning Workflow: from Anonymization to Personalized Differential Privacy Budgets in Federated Learning](https://mlsanity.com/paper/2605.02372/) (2026)

## Abstract (arXiv)

Open science is a fundamental pillar to promote scientific progress and collaboration, based on the principles of open data, open source and open access. However, the requirements for publishing and sharing open data are in many cases difficult to meet in compliance with strict data protection regulations. Consequently, researchers need to rely on proven methods that allow them to anonymize their data without sharing it with third parties. To this end, this paper presents the implementation of a Python library for the anonymization of sensitive tabular data. This framework provides users with a wide range of anonymization methods that can be applied on the given dataset, including the set of identifiers, quasi-identifiers, generalization hierarchies and allowed level of suppression, along with the sensitive attribute and the level of anonymity required. The library has been implemented following best practices for integration and continuous development, as well as the use of workflows to test code coverage based on unit and functional tests.