Which anonymization technique is best for which NLP task? -- It depends. A Systematic Study on Clinical Text Processing
This work addresses the challenge of balancing data privacy and utility in clinical NLP for researchers and practitioners, but it is incremental as it confirms known trade-offs without introducing new methods.
The study systematically evaluates how different anonymization techniques affect machine learning model performance across five NLP tasks using clinical text datasets, finding that stronger anonymization significantly reduces performance and most techniques are vulnerable to re-identification attacks.
Clinical text processing has gained more and more attention in recent years. The access to sensitive patient data, on the other hand, is still a big challenge, as text cannot be shared without legal hurdles and without removing personal information. There are many techniques to modify or remove patient related information, each with different strengths. This paper investigates the influence of different anonymization techniques on the performance of ML models using multiple datasets corresponding to five different NLP tasks. Several learnings and recommendations are presented. This work confirms that particularly stronger anonymization techniques lead to a significant drop of performance. In addition to that, most of the presented techniques are not secure against a re-identification attack based on similarity search.