  Data privacy is an increasingly important aspect of many real-world Data
sources that contain sensitive information may have immense potential which
could be unlocked using the right privacy enhancing transformations, but
current methods often fail to produce convincing output. Furthermore, finding
the right balance between privacy and utility is often a tricky trade-off. In
this work, we propose a novel approach for data privatization, which involves
two steps: in the first step, it removes the sensitive information, and in the
second step, it replaces this information with an independent random sample.
Our method builds on adversarial representation learning which ensures strong
privacy by training the model to fool an increasingly strong adversary. While
previous methods only aim at obfuscating the sensitive information, we find
that adding new random information in its place strengthens the provided
privacy and provides better utility at any given level of privacy. The result
is an approach that can provide stronger privatization on image data, and yet
be preserving both the domain and the utility of the inputs, entirely
independent of the downstream task.
