  Large and well-annotated datasets are essential for advancing deep learning
applications, however often costly or impossible to obtain by a single entity.
In many areas, including the medical domain, approaches relying on data sharing
have become critical to address those challenges. While effective in increasing
dataset size and diversity, data sharing raises significant privacy concerns.
Commonly employed anonymization methods based on the k-anonymity paradigm often
fail to preserve data diversity, affecting model robustness. This work
introduces a novel approach using Conditional Variational Autoencoders (CVAEs)
trained on feature vectors extracted from large pre-trained vision foundation
models. Foundation models effectively detect and represent complex patterns
across diverse domains, allowing the CVAE to faithfully capture the embedding
space of a given data distribution to generate (sample) a diverse,
privacy-respecting, and potentially unbounded set of synthetic feature vectors.
Our method notably outperforms traditional approaches in both medical and
natural image domains, exhibiting greater dataset diversity and higher
robustness against perturbations while preserving sample privacy. These results
underscore the potential of generative models to significantly impact deep
learning applications in data-scarce and privacy-sensitive environments. The
source code is available at
https://github.com/francescodisalvo05/cvae-anonymization .
