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

2025/10/17 by Catherine Diaz‐Asper, Lars Ailo Bongo, Brita Elvevåg · 1 voice
Computer Science · #Privacy-Preserving Technologies in Data

paper · pdf · doi:10.1038/s41746-025-01987-3

openalex publication_date 2025/10/17 · openalex created_date 2025/10/18 · openalex updated_date 2026/07/23

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.

Citations

• The VoicePrivacy 2022 Challenge: Progress and Perspectives in Voice Anonymisation
• How to keep text private? A systematic review of deep learning methods for privacy-preserving natural language processing

Discussions

• 💰🔐 Diaz-Asper, C., Bongo, L. A., & Elvevåg, B. (2025). Navigating the tradeoff between personal #privacy and data utility in speech anonymization for clinical research. NPJ digital medicine, 8(1), 6 [bsky, 2 points, 0 comments]

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