Content-Aware Differential Privacy with Conditional Invertible Neural Networks. (arXiv:2207.14625v1 [cs.CR])

Differential privacy (DP) has arisen as the gold standard in protecting an
individual's privacy in datasets by adding calibrated noise to each data
sample. While the application to categorical data is straightforward, its
usability in the context of images has been limited. Contrary to categorical
data the meaning of an image is inherent in the spatial correlation of
neighboring pixels making the simple application of noise infeasible.
Invertible Neural Networks (INN) have shown excellent generative performance
while still providing the …

differential privacy networks neural networks privacy