mlsanityData to 7 Oct 2026

Cloaked Classifiers: Pseudonymization Strategies on Sensitive Classification Tasks

arXiv 2406.17875 ↗First posted 25 Jun 2024cs.CLcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 15 of its 57 references that are in the map. First posted on arXiv 25 Jun 2024.

Builds on

15 of 57 references.

References · 15

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  10. 2022Multilingual Auxiliary Tasks Training: Bridging the Gap between Languages for Zero-Shot Transfer of Hate Speech Detection Modelscited by 10
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  12. 2019Understanding the Radical Mind: Identifying Signals to Detect Extremist Content on Twittercited by 4
3 more
  1. 2020Establishing a New State-of-the-Art for French Named Entity Recognitioncited by 3
  2. 2021A survey on extremism analysis using Natural Language Processingcited by 3
  3. 2022How Much User Context Do We Need? Privacy by Design in Mental Health NLP Applicationcited by 2

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Cited by year

Citing papers per year: 2024: 1; 2025: 2.

3 papers.

  1. 2025Tau-Eval: A Unified Evaluation Framework for Useful and Private Text Anonymizationcited by 4
  2. 2024Beyond Dataset Creation: Critical View of Annotation Variation and Bias Probing of a Dataset for Online Radical Content Detectioncited by 3
  3. 2025IYKYK: Using language models to decode extremist cryptolectscited by 3