A privacy stack for speech-based AI in digital health

npj Digital Public Health, vol. 1

Abstract

Abstract Speech is an accessible and information-rich clinical signal, but its diagnostic value is deeply entwined with biometric identity. Empirical and perceptual evidence shows that current anonymization methods cannot fully remove identity cues and often introduces disorder-dependent artifacts. These limitations reveal a need for a system-level approach. We propose a privacy stack that reconceives privacy as identity uncertainty, achieved through coordinated signal diversification, leakage-resistant model design, and privacy-aware infrastructure. This framework outlines a path toward clinically meaningful, equitable, and trustworthy speech-based artificial intelligence systems.

Authors 1

1. Soroosh Tayebi Arasteh corresponding Aachen Department of Diagnostic and Interventional Radiology Lab for AI in Medicine

   RWTH Aachen University · Universitätsklinikum Aachen · Friedrich-Alexander-Universität Erlangen-Nürnberg

   Affiliation as printed

   Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany

   Lab for AI in Medicine, RWTH Aachen University, Aachen, Germany

   Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany