k-Same-Net: k-Anonymity with Generative Deep Neural Networks for Face Deidentification.Image and video data are today being shared between government entities and other relevant stakeholders on a regular basis and require careful handling of the personal information contained therein.  In this work, we propose a novel approach towards face deidentification, called k-Same-Net, which combines recent Generative Neural Networks (GNNs) with the well-known k-Anonymitymechanism and provides formal guarantees regarding privacy protection on a closed set of identities.  We demonstrate the feasibility of k-Same-Net in comprehensive experiments on the XM2VTS and CK+ datasets.  We also present facial expression recognition experiments to demonstrate the utility-preservation capabilities of k-Same-Net.  Our experimental results suggest that k-Same-Net is a viable option for facial deidentification that exhibits several desirable characteristics when compared to existing solutions in this area.
k-Same-Net
In this work, we propose a novel approach towards face deidentification, called k-Same-Net, which combines recent Generative Neural Networks (GNNs) with the well-known k-Anonymitymechanism and provides formal guarantees regarding privacy protection on a closed set of identities. 
Generative Neural Networks (GNNs)
In this work, we propose a novel approach towards face deidentification, called k-Same-Net, which combines recent Generative Neural Networks (GNNs) with the well-known k-Anonymitymechanism and provides formal guarantees regarding privacy protection on a closed set of identities. 
k-Anonymity mechanism