IH&MMSEC 2023, ACM Workshop on Information Hiding and Multimedia Security, 28-30 June 2023, Chicago, USA
Over the last decade, the use of Automatic Speaker Verification
(ASV) systems has become increasingly widespread in response
to the growing need for secure and efficient identity verification
methods. The voice data encompasses a wealth of personal information,
which includes but is not limited to gender, age, health
condition, stress levels, and geographical and socio-cultural origins.
These attributes, known as soft biometrics, are private and
the user may wish to keep them confidential. However, with the
advancement of machine learning algorithms, soft biometrics can
be inferred automatically, creating the potential for unauthorized
use. As such, it is crucial to ensure the protection of these personal
data that are inherent within the voice while retaining the utility
of identity recognition. In this paper, we present an adversarial
Auto-Encoder–based approach to hide gender-related information
in speaker embeddings, while preserving their effectiveness for
speaker verification. We use an adversarial procedure against a
gender classifier and incorporate a layer based on the Laplace mechanism
into the Auto-Encoder architecture. This layer adds Laplace
noise for more robust gender concealment and ensures differential
privacy guarantees during inference for the output speaker embeddings.
Experiments conducted on the VoxCeleb dataset demonstrate
that speaker verification tasks can be effectively carried out while
concealing speaker gender and ensuring differential privacy guarantees;
moreover, the intensity of the Laplace noise can be tuned
to select the desired trade-off between privacy and utility.
Type:
Poster / Demo
City:
Chicago
Date:
2023-06-28
Department:
Sécurité numérique
Eurecom Ref:
7351
Copyright:
© ACM, 2023. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in IH&MMSEC 2023, ACM Workshop on Information Hiding and Multimedia Security, 28-30 June 2023, Chicago, USA https://doi.org/10.1145/3577163.3595102
