  The growing richness of large-scale datasets has been crucial in driving the
rapid advancement and wide adoption of machine learning technologies. The
massive collection and usage of data, however, pose an increasing risk for
people's private and sensitive information due to either inadvertent
mishandling or malicious exploitation. Besides legislative solutions, many
technical approaches have been proposed towards data privacy protection.
However, they bear various limitations such as leading to degraded data
availability and utility, or relying on heuristics and lacking solid
theoretical bases. To overcome these limitations, we propose a formal
information-theoretic definition for this utility-preserving privacy protection
problem, and design a data-driven learnable data transformation framework that
is capable of selectively suppressing sensitive attributes from target datasets
while preserving the other useful attributes, regardless of whether or not they
are known in advance or explicitly annotated for preservation. We provide
rigorous theoretical analyses on the operational bounds for our framework, and
carry out comprehensive experimental evaluations using datasets of a variety of
modalities, including facial images, voice audio clips, and human activity
motion sensor signals. Results demonstrate the effectiveness and
generalizability of our method under various configurations on a multitude of
tasks. Our code is available at https://github.com/jpmorganchase/MaSS.
