  Recent deep learning models have shown remarkable performance in image
classification. While these deep learning systems are getting closer to
practical deployment, the common assumption made about data is that it does not
carry any sensitive information. This assumption may not hold for many
practical cases, especially in the domain where an individual's personal
information is involved, like healthcare and facial recognition systems. We
posit that selectively removing features in this latent space can protect the
sensitive information and provide a better privacy-utility trade-off.
Consequently, we propose DISCO which learns a dynamic and data driven pruning
filter to selectively obfuscate sensitive information in the feature space. We
propose diverse attack schemes for sensitive inputs \& attributes and
demonstrate the effectiveness of DISCO against state-of-the-art methods through
quantitative and qualitative evaluation. Finally, we also release an evaluation
benchmark dataset of 1 million sensitive representations to encourage rigorous
exploration of novel attack schemes.
