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arXiv · 2406.17875

Cloaked Classifiers: Pseudonymization Strategies on Sensitive Classification Tasks

Arij Riabi·Menel Mahamdi·Virginie Mouilleron·Djamé Seddah
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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.

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Arij Riabi, Menel Mahamdi, Virginie Mouilleron, Djamé Seddah. 2024-06-25. Cloaked Classifiers: Pseudonymization Strategies on Sensitive Classification Tasks. https://arxiv.org/abs/2406.17875

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