Papers with re-identification
Clinical Text Anonymization, its Influence on Downstream NLP Tasks and the Risk of Re-Identification (2023.eacl-srw)
Copied to clipboard
| Challenge: | De-identification and anonymization of clinical data is needed to solve access to clinicaldata. |
| Approach: | They propose to use text anonymization techniques to break the anonymization of clinical data . they propose to apply a re-identification attack to the anonymized text data to break this. |
| Outcome: | The proposed approach can break the anonymization of clinical data, the authors show . |
Neural Text Sanitization with Explicit Measures of Privacy Risk (2022.aacl-main)
Copied to clipboard
| Challenge: | Personal data, also known as Personally Identifiable Information (PII), often abound in text documents. |
| Approach: | They propose a method for text sanitization that uses a neural entity recognizer to detect and classify potential personal identifiers and a classifier trained on labelled data to determine which entities to mask . |
| Outcome: | The proposed approach masks all personal identifiers and thereby conceals the identity of the individuals mentioned in the document. |
Re-identification of De-identified Documents with Autoregressive Infilling (2025.acl-long)
Copied to clipboard
| Challenge: | Documents revealing sensitive information about individuals must typically be de-identified by masking all mentions of PII . |
| Approach: | They propose a method that attempts to reverse the process of re-identification based on a database of documents representing background knowledge. |
| Outcome: | The proposed method can recover as many as 80% of de-identified text spans and improve accuracy along with the level of background knowledge. |
Anonymity at Risk? Assessing Re-Identification Capabilities of Large Language Models in Court Decisions (2024.findings-naacl)
Copied to clipboard
| Challenge: | Despite high re-identification rates on Wikipedia, even the best LLMs struggled with court decisions. |
| Approach: | They construct an anonymized Wikipedia dataset to investigate re-identification risks . they also introduce new metrics to measure performance . |
| Outcome: | The proposed model can be used to identify individuals in court decisions, but it fails in the vast majority of cases. |
Robust Utility-Preserving Text Anonymization Based on Large Language Models (2025.acl-long)
Copied to clipboard
| Challenge: | Existing techniques face challenges of re-identification ability of large language models . anonymizing text that contains sensitive information is crucial for a wide range of applications . |
| Approach: | They propose a framework that integrates three key LLM components to perform anonymization. |
| Outcome: | The proposed model outperforms baselines while maintaining greater data utility in downstream tasks. |