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An Open Source Python Library for Anonymizing Sensitive Data.

Judith Sáinz-Pardo Díaz¹, Álvaro López García²

¹Instituto de Física de Cantabria (IFCA), CSIC-UC Avda. los Castros s/n, 39005, Santander, Spain. sainzpardo@ifca.unican.es.

Scientific Data

|November 26, 2024

Summary

This study introduces a Python library for anonymizing sensitive tabular data, enabling researchers to comply with data protection regulations while advancing open science principles. The tool offers various anonymization methods to ensure data privacy without sharing raw information.

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Area of Science:

Computer Science
Data Privacy
Scientific Research Methodology

Background:

Open science principles (open data, open source, open access) are crucial for scientific progress and collaboration.
Strict data protection regulations pose challenges for publishing and sharing sensitive open data.
Researchers require robust methods for data anonymization to protect privacy without compromising data utility.

Purpose of the Study:

To present a Python library designed for the anonymization of sensitive tabular data.
To provide researchers with a flexible framework for applying various anonymization techniques.
To facilitate compliance with data protection regulations in the context of open science.

Main Methods:

Implementation of a Python library incorporating multiple anonymization techniques.
Support for defining identifiers, quasi-identifiers, generalization hierarchies, and suppression levels.
Integration of sensitive attributes and required anonymity levels for tailored anonymization.

Main Results:

A functional Python library for tabular data anonymization is developed.
The library offers a comprehensive suite of methods to address diverse data privacy needs.
The implementation adheres to best practices for software development and testing.

Conclusions:

The developed Python library effectively addresses the challenge of anonymizing sensitive data for open science.
Researchers can utilize this tool to share data responsibly while maintaining compliance with privacy laws.
The library promotes secure data sharing and enhances the practice of open science.