mlsanityData to 7 Oct 2026

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

arXiv 2306.05561 ↗First posted 8 Jun 2023cs.CLcited by 18

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Abstract (arXiv)

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

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 11 of its 41 references that are in the map. First posted on arXiv 8 Jun 2023.

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11 of 41 references.

References · 11

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  4. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  5. 2016Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyondcited by 809
  6. 2023DeID-GPT: Zero-shot Medical Text De-Identification by GPT-4cited by 128
  7. 2022The Text Anonymization Benchmark (TAB): A Dedicated Corpus and Evaluation Framework for Text Anonymizationcited by 94
  8. 2016De-identification of Patient Notes with Recurrent Neural Networkscited by 88
  9. 2021Differential Privacy for Text Analytics via Natural Text Sanitizationcited by 74
  10. 2014C-sanitized: a privacy model for document redaction and sanitizationcited by 27
  11. 2022Bootstrapping Text Anonymization Models with Distant Supervisioncited by 8

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Citing papers per year: 2023: 2; 2024: 2; 2025: 7; 2026: 7.

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  1. 2023Jais and Jais-chat: Arabic-Centric Foundation and Instruction-Tuned Open Generative Large Language Modelscited by 68
  2. 2024Evaluating Differentially Private Synthetic Data Generation in High-Stakes Domainscited by 12
  3. 2024Robust Utility-Preserving Text Anonymization Based on Large Language Modelscited by 9
  4. 2025A Survey on Current Trends and Recent Advances in Text Anonymizationcited by 5
  5. 2023Neural Text Sanitization with Privacy Risk Indicators: An Empirical Analysiscited by 4
  6. 2025A General Pseudonymization Framework for Cloud-Based LLMs: Replacing Privacy Information in Controlled Text Generationcited by 3
  7. 2025PATCH: Mitigating PII Leakage in Language Models with Privacy-Aware Targeted Circuit PatcHingcited by 2
  8. 2025User Perceptions vs. Proxy LLM Judges: Privacy and Helpfulness in LLM Responses to Privacy-Sensitive Scenarioscited by 1
  9. 2026Adaptive Text Anonymization: Learning Privacy-Utility Trade-offs via Prompt Optimizationcited by 1
  10. 2025Current State in Privacy-Preserving Text Preprocessing for Domain-Agnostic NLPcited by 0
  11. 2025A Study of Privacy-preserving Language Modeling Approachescited by 0
  12. 2025An Artificial Intelligence Value at Risk Approach: Metrics and Modelscited by 0
6 more
  1. 2026Say Something Else: Rethinking Contextual Privacy as Information Sufficiencycited by 0
  2. 2026Surrogate Substitution Preserves PHI Detectability: A Multi-Detector Equivalence Studycited by 0
  3. 2026From Interpretability to Control: Insights from Six Years of the TrustNLP Workshopcited by 0
  4. 2026No PUN Intended: Plausible Unknown Names for Person-Centred LLM Evaluationcited by 0
  5. 2026PrivBench: A Holistic and Modular Benchmarking Platform for Evaluating Text-to-Text Privatizationcited by 0
  6. 2026On the Impact of Anonymization on the Performance of Large Language Modelscited by 0