Papers with de-identification techniques
Downstream Task Performance of BERT Models Pre-Trained Using Automatically De-Identified Clinical Data (2022.lrec-1)
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| Challenge: | Automatic de-identification systems introduce errors due to their imperfect precision and may negatively impact the utility of the de-identified dataset. |
| Approach: | They propose to de-identifie a large clinical corpus in Swedish by removing entire sentences containing sensitive data or by replacing sensitive words with realistic surrogates. |
| Outcome: | The proposed models are safe to distribute to other academic researchers and reduce privacy risks. |
Zero-Shot Privacy-Aware Text Rewriting via Iterative Tree Search (2025.findings-emnlp)
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| Challenge: | Existing methods for text anonymization and de-identification struggle to balance privacy preservation with text naturalness and utility. |
| Approach: | They propose a tree-search-based iterative sentence rewriting algorithm that obfuscates or deletes private information while preserving coherence, relevance, and naturalness. |
| Outcome: | The proposed algorithm outperforms existing baselines on privacy-sensitive datasets. |