Cloaked Classifiers: Pseudonymization Strategies on Sensitive Classification Tasks

TL;DR

This paper shares the method for manually pseudonymizing a multilingual radicalization dataset, ensuring performance comparable to the original data, and highlights the importance of establishing comprehensive guidelines for processing sensitive NLP data.

Abstract

Protecting privacy is essential when sharing data, particularly in the case of an online radicalization dataset that may contain personal information. In this paper, we explore the balance between preserving data usefulness and ensuring robust privacy safeguards, since regulations like the European GDPR shape how personal information must be handled. We share our method for manually pseudonymizing a multilingual radicalization dataset, ensuring performance comparable to the original data. Furthermore, we highlight the importance of establishing comprehensive guidelines for processing sensitive NLP data by sharing our complete pseudonymization process, our guidelines, the challenges we encountered as well as the resulting dataset.

Figures (2)

• Figure 1: Data source and call for action distributions for English, French, and Arabic
• Figure 2: Types of anonymized data in French and English