mlsanityData to 14 Oct 2026

Neural Text Sanitization with Privacy Risk Indicators: An Empirical Analysis

arXiv 2310.14312 ↗First posted 22 Oct 2023cs.CLcited by 5

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Abstract (arXiv)

Text sanitization is the task of redacting a document to mask all occurrences of (direct or indirect) personal identifiers, with the goal of concealing the identity of the individual(s) referred in it. In this paper, we consider a two-step approach to text sanitization and provide a detailed analysis of its empirical performance on two recently published datasets: the Text Anonymization Benchmark (Pil'an et al., 2022) and a collection of Wikipedia biographies (Papadopoulou et al., 2022). The text sanitization process starts with a privacy-oriented entity recognizer that seeks to determine the text spans expressing identifiable personal information. This privacy-oriented entity recognizer is trained by combining a standard named entity recognition model with a gazetteer populated by person-related terms extracted from Wikidata. The second step of the text sanitization process consists in assessing the privacy risk associated with each detected text span, either isolated or in combination with other text spans. We present five distinct indicators of the re-identification risk, respectively based on language model probabilities, text span classification, sequence labelling, perturbations, and web search. We provide a contrastive analysis of each privacy indicator and highlight their benefits and limitations, notably in relation to the available labeled data.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 25 of its 60 references that are in the map. First posted on arXiv 22 Oct 2023.

Builds on

25 of 60 references.

References · 25

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13 more
  1. 2016De-identification of Patient Notes with Recurrent Neural Networkscited by 88
  2. 2018Generalised Differential Privacy for Text Document Processingcited by 52
  3. 2019Leveraging Hierarchical Representations for Preserving Privacy and Utility in Textcited by 50
  4. 2021ADePT: Auto-encoder based Differentially Private Text Transformationcited by 31
  5. 2023DP-BART for Privatized Text Rewriting under Local Differential Privacycited by 28
  6. 2014C-sanitized: a privacy model for document redaction and sanitizationcited by 27
  7. 2019Saliency-driven Word Alignment Interpretation for Neural Machine Translationcited by 27
  8. 2021When differential privacy meets NLP: The devil is in the detailcited by 25
  9. 2020Data Minimization for GDPR Compliance in Machine Learning Modelscited by 21
  10. 2023Privacy- and Utility-Preserving NLP with Anonymized Data: A case study of Pseudonymizationcited by 18
  11. 2021No Intruder, no Validity: Evaluation Criteria for Privacy-Preserving Text Anonymizationcited by 13
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  13. 2018A survey of automatic de-identification of longitudinal clinical narrativescited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 3.

5 papers.

  1. 2026Privasis: Synthesizing the Largest "Public" Private Dataset from Scratchcited by 3
  2. 2026Subject-level Inference for Realistic Text Anonymization Evaluationcited by 1
  3. 2024Preempting Text Sanitization Utility in Resource-Constrained Privacy-Preserving LLM Interactionscited by 0
  4. 2025CTIGuardian: A Few-Shot Framework for Mitigating Privacy Leakage in Fine-Tuned LLMscited by 0
  5. 2026PrivBench: A Holistic and Modular Benchmarking Platform for Evaluating Text-to-Text Privatizationcited by 0