Privacy-preserving feature extractor using adversarial pruning for TBI assessment from speech

Speech is an effective indicator of medical conditions such as Traumatic Brain Injury (TBI), but frequently includes private information, preventing novel passive, real-world assessments using the patient’s smartphone. Privacy research for speech processing has primarily focused on hiding the speaker’s identity, which is utilized in authentication systems and cannot be renewed. Our study extends privacy to include the content of speech, specifically sensitive words during conversation. Prior work has proposed extracting privacy-preserving features via adversarial training, which trains a neural network to defend against attacks on private data that an adversarial network is simultaneously attempting to access. However, adversarial training has an unsolved problem of training instability due to the inherent limitations of minimax optimization. Instead, our study introduces Privacy-Preserving using Adversarial Pruning (PPA-Pruning). Nodes are systematically removed from the network while prioritizing those contributing most to the recognition of personal data from a well-trained feature extractor designed for TBI detection and adversarial tasks. PPA-Pruning was evaluated for various privacy budgets via a differential privacy setup. Notably, PPA-Pruning outperforms baseline methods, including adversarial training and Laplace noise, achieving up to an 11% improvement in TBI detection accuracy at the same privacy level.