  Integration of speech into healthcare has intensified privacy concerns due to
its potential as a non-invasive biomarker containing individual biometric
information. In response, speaker anonymization aims to conceal personally
identifiable information while retaining crucial linguistic content. However,
the application of anonymization techniques to pathological speech, a critical
area where privacy is especially vital, has not been extensively examined. This
study investigates anonymization's impact on pathological speech across over
2,700 speakers from multiple German institutions, focusing on privacy,
pathological utility, and demographic fairness. We explore both
deep-learning-based and signal processing-based anonymization methods, and
document substantial privacy improvements across disorders-evidenced by equal
error rate increases up to 1933%, with minimal overall impact on utility.
Specific disorders such as Dysarthria, Dysphonia, and Cleft Lip and Palate
experienced minimal utility changes, while Dysglossia showed slight
improvements. Our findings underscore that the impact of anonymization varies
substantially across different disorders. This necessitates disorder-specific
anonymization strategies to optimally balance privacy with diagnostic utility.
Additionally, our fairness analysis revealed consistent anonymization effects
across most of the demographics. This study demonstrates the effectiveness of
anonymization in pathological speech for enhancing privacy, while also
highlighting the importance of customized and disorder-specific approaches to
account for inversion attacks.
