Cloaked Classifiers: Pseudonymization Strategies on Sensitive Classification Tasks

TL;DR

This paper presents a pseudonymization method for sensitive multilingual radicalization data, balancing privacy protection with data utility, and provides comprehensive guidelines and insights for handling NLP data under privacy regulations.

Contribution

It introduces a manual pseudonymization strategy for sensitive NLP datasets and shares detailed guidelines and challenges encountered.

Findings

Performance comparable to original data after pseudonymization

Detailed pseudonymization process and guidelines shared

Highlights importance of privacy-preserving NLP data handling

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.