  Generative models are gaining significant attention as potential catalysts
for a novel industrial revolution. Since automated sample generation can be
useful to solve privacy and data scarcity issues that usually affect learned
biometric models, such technologies became widely spread in this field. In this
paper, we assess the vulnerabilities of generative machine learning models
concerning identity protection by designing and testing an identity inference
attack on fingerprint datasets created by means of a generative adversarial
network. Experimental results show that the proposed solution proves to be
effective under different configurations and easily extendable to other
biometric measurements.
