  This study investigates the impact of gender information on utility, privacy,
and fairness in voice biometric systems, guided by the General Data Protection
Regulation (GDPR) mandates, which underscore the need for minimizing the
processing and storage of private and sensitive data, and ensuring fairness in
automated decision-making systems. We adopt an approach that involves the
fine-tuning of the wav2vec 2.0 model for speaker verification tasks, evaluating
potential gender-related privacy vulnerabilities in the process. Gender
influences during the fine-tuning process were employed to enhance fairness and
privacy in order to emphasise or obscure gender information within the
speakers' embeddings. Results from VoxCeleb datasets indicate our adversarial
model increases privacy against uninformed attacks, yet slightly diminishes
speaker verification performance compared to the non-adversarial model.
However, the model's efficacy reduces against informed attacks. Analysis of
system performance was conducted to identify potential gender biases, thus
highlighting the need for further research to understand and improve the
delicate interplay between utility, privacy, and equity in voice biometric
systems.
