  Attribute-driven privacy aims to conceal a single user's attribute, contrary
to anonymisation that tries to hide the full identity of the user in some data.
When the attribute to protect from malicious inferences is binary, perfect
privacy requires the log-likelihood-ratio to be zero resulting in no
strength-of-evidence. This work presents an approach based on normalizing flow
that maps a feature vector into a latent space where the evidence, related to
the binary attribute, and an independent residual are disentangled. It can be
seen as a non-linear discriminant analysis where the mapping is invertible
allowing generation by mapping the latent variable back to the original space.
This framework allows to manipulate the log-likelihood-ratio of the data and
therefore allows to set it to zero for privacy. We show the applicability of
the approach on an attribute-driven privacy task where the sex information is
removed from speaker embeddings. Results on VoxCeleb2 dataset show the
efficiency of the method that outperforms in terms of privacy and utility our
previous experiments based on adversarial disentanglement.
