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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月17日

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  • Catherine Diaz-Asper,
  • Lars Ailo Bongo,
  • Brita Elvevåg

摘要

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.