Navigating the tradeoff between personal privacy and data utility in speech anonymization for clinical research

Speech data inherently contains personally identifiable information. Anonymization strategies to obscure this while preserving essential characteristics all represent a tradeoff between privacy and utility. We examine this balancing act of modifying voice characteristics, masking identity, and eliminating identifiable content by showcasing challenges with the common techniques-generalization, suppression, anatomization, permutation, and perturbation-in the context of preserving utility for individual level speech data analyses in clinical research.