Navigating the tradeoff between personal privacy and data utility in speech anonymization for clinical research.
review · Level V
Where this comes from
- Record sourced from PubMed, PMID 41107524.
- Also identified by DOI 10.1038/s41746-025-01987-3 and PMC identifier 12534620.
- Licence recorded as CC BY-NC-ND.
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Abstract
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.