Pseudonymiser manuellement des données sensibles

Le résumé :

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.

En s'appuyant sur les recommandations de la CNIL et du RGPD, les auteurs revisitent la littérature sur l'anonymisation et la pseudonymisation des données pour élaborer une méthodologie pour pseudonymiser un jeu de données sur la radicalisation (particulièrement sensible, donc).

Citation : Arij Riabi, Menel Mahamdi, Virginie Mouilleron, and Djamé Seddah. 2024. Cloaked Classifiers: Pseudonymization Strategies on Sensitive Classification Tasks. In Proceedings of the Fifth Workshop on Privacy in Natural Language Processing, pages 123–136, Bangkok, Thailand. Association for Computational Linguistics.