  State-of-the-art machine learning algorithms can be fooled by carefully
crafted adversarial examples. As such, adversarial examples present a concrete
problem in AI safety. In this work we turn the tables and ask the following
question: can we harness the power of adversarial examples to prevent malicious
adversaries from learning identifying information from data while allowing
non-malicious entities to benefit from the utility of the same data? For
instance, can we use adversarial examples to anonymize biometric dataset of
faces while retaining usefulness of this data for other purposes, such as
emotion recognition? To address this question, we propose a simple yet
effective method, called Siamese Generative Adversarial Privatizer (SGAP), that
exploits the properties of a Siamese neural network to find discriminative
features that convey identifying information. When coupled with a generative
model, our approach is able to correctly locate and disguise identifying
information, while minimally reducing the utility of the privatized dataset.
Extensive evaluation on a biometric dataset of fingerprints and cartoon faces
confirms usefulness of our simple yet effective method.
