  We propose an efficient solver for the privacy funnel (PF) method, leveraging
its difference-of-convex (DC) structure. The proposed DC separation results in
a closed-form update equation, which allows straightforward application to both
known and unknown distribution settings. For known distribution case, we prove
the convergence (local stationary points) of the proposed non-greedy solver,
and empirically show that it outperforms the state-of-the-art approaches in
characterizing the privacy-utility trade-off. The insights of our DC approach
apply to unknown distribution settings where labeled empirical samples are
available instead. Leveraging the insights, our alternating minimization solver
satisfies the fundamental Markov relation of PF in contrast to previous
variational inference-based solvers. Empirically, we evaluate the proposed
solver with MNIST and Fashion-MNIST datasets. Our results show that under a
comparable reconstruction quality, an adversary suffers from higher prediction
error from clustering our compressed codes than that with the compared methods.
Most importantly, our solver is independent to private information in inference
phase contrary to the baselines.
