  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.
