  In this work, we propose information laundering, a novel framework for
enhancing model privacy. Unlike data privacy that concerns the protection of
raw data information, model privacy aims to protect an already-learned model
that is to be deployed for public use. The private model can be obtained from
general learning methods, and its deployment means that it will return a
deterministic or random response for a given input query. An
information-laundered model consists of probabilistic components that
deliberately maneuver the intended input and output for queries to the model,
so the model's adversarial acquisition is less likely. Under the proposed
framework, we develop an information-theoretic principle to quantify the
fundamental tradeoffs between model utility and privacy leakage and derive the
optimal design.
