Privacy- and Utility-Preserving NLP with Anonymized Data: A case study of Pseudonymization

Oleksandr Yermilov, Vipul Raheja, Artem Chernodub
6/8/2023
cs.CL

No official code found. Best implementation: olexandryermilov/privacy-preserving-nlp (7 stars, last commit 2023-09-08).

Abstract

This work investigates the effectiveness of different pseudonymization techniques, ranging from rule-based substitutions to using pre-trained Large Language Models (LLMs), on a variety of datasets and models used for two widely used NLP tasks: text classification and summarization. Our work provides crucial insights into the gaps between original and anonymized data (focusing on the pseudonymization technique) and model quality and fosters future research into higher-quality anonymization techniques to better balance the trade-offs between data protection and utility preservation. We make our code, pseudonymized datasets, and downstream models publicly available

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Code Implementations(1)

olexandryermilov/privacy-preserving-nlpCommunity100%
71Shell, PythonJun 1, 20233 years ago

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Cite this paper

@article{yermilov2023privacy,
  title  = {Privacy- and Utility-Preserving NLP with Anonymized Data: A case study of Pseudonymization},
  author = {Oleksandr Yermilov and Vipul Raheja and Artem Chernodub},
  year   = {2023},
  eprint = {2306.05561},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2306.05561v1}
}

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