Abstract
Currently, voice-related privacy risks are studied under the assumption that the attacker has access only to spoken audio. In contrast, real-world collections often pair the speech signal with side information; yet to our knowledge, no publicly available dataset provides sufficiently detailed and directly linkable speech and side-information pairs to evaluate the combined risk. We propose a distribution-constrained method to build a semi-synthetic dataset and study singling out risk when geographic side information is assumed to accompany anonymized speech. We instantiate it using the English portion of Mozilla Common Voice and the 2021 UK Census, under ignorant and semi-informed attackers, and introduce a conditional reference that isolates the contribution of the audio beyond the candidate-set reduction induced by the side information. In our controlled geographic case study, for single-utterance speakers, the semi-informed attacker reaches an observed isolation probability of 0.718 in the finest OA-plus-household setting, compared with 0.380 in the signal-only setting; its excess over the conditional reference increases from 0.012 to 0.340. The ignorant attacker shows the same trend at a lower level. The effect is largest at fine granularity and short recordings, precisely the setting in which signal-only evaluation looks safest. Our results show that geographic side information can increase the singling out risk of anonymized speech, and we recommend including attacker settings with side information in future studies of voice privacy.
Abstract (translated)
URL
https://arxiv.org/abs/2610.26884
