A Privacy-Preserving Walk in the Latent Space of Generative Models for
  Medical Applications
Matteo Pennisi Federica Proietto Salanitri Giovanni Bellitto Simone Palazzo Ulas Bagci Concetto Spampinato
Generative Adversarial Networks (GANs) have demonstrated their ability to
generate synthetic samples that match a target distribution. However, from a
privacy perspective, using GANs as a proxy for data sharing is not a safe
solution, as they tend to embed near-duplicates of real samples in the latent
space. Recent works, inspired by k-anonymity principles, address this issue
through sample aggregation in the latent space, with the drawback of reducing
the dataset by a factor of k. Our work aims to mitigate this problem by
proposing a latent space navigation strategy able to generate diverse synthetic
samples that may support effective training of deep models, while addressing
privacy concerns in a principled way. Our approach leverages an auxiliary
identity classifier as a guide to non-linearly walk between points in the
latent space, minimizing the risk of collision with near-duplicates of real
samples. We empirically demonstrate that, given any random pair of points in
the latent space, our walking strategy is safer than linear interpolation. We
then test our path-finding strategy combined to k-same methods and demonstrate,
on two benchmarks for tuberculosis and diabetic retinopathy classification,
that training a model using samples generated by our approach mitigate drops in
performance, while keeping privacy preservation.