  As more and more data is collected in various settings across organizations,
companies, and countries, there has been an increase in the demand of user
privacy. Developing privacy preserving methods for data analytics is thus an
important area of research. In this work we present a model based on generative
adversarial networks (GANs) that learns to obfuscate specific sensitive
attributes in speech data. We train a model that learns to hide sensitive
information in the data, while preserving the meaning in the utterance. The
model is trained in two steps: first to filter sensitive information in the
spectrogram domain, and then to generate new and private information
independent of the filtered one. The model is based on a U-Net CNN that takes
mel-spectrograms as input. A MelGAN is used to invert the spectrograms back to
raw audio waveforms. We show that it is possible to hide sensitive information
such as gender by generating new data, trained adversarially to maintain
utility and realism.
