  Data is used widely by service providers as input to inference systems to
perform decision making for authorized tasks. The raw data however allows a
service provider to infer other sensitive information it has not been
authorized for. We propose a data-driven inference privacy preserving framework
to sanitize data so as to prevent leakage of sensitive information that is
present in the raw data, while ensuring that the sanitized data is still
compatible with the service provider's legacy inference system. We develop an
inference privacy framework based on the variational method and include maximum
mean discrepancy and domain adaption as techniques to regularize the domain of
the sanitized data to ensure its legacy compatibility. However, the variational
method leads to weak privacy in cases where the underlying data distribution is
hard to approximate. It may also face difficulties when handling continuous
private variables. To overcome this, we propose an alternative formulation of
the privacy metric using maximal correlation and we present empirical methods
to estimate it. Finally, we develop a deep learning model as an example of the
proposed inference privacy framework. Numerical experiments verify the
feasibility of our approach.
