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.

翻译：我们提出了一种高效的隐私漏斗（PF）方法求解器，充分利用其差分凸（DC）结构。所提出的DC分解导出了闭合形式的更新方程，可直接应用于已知和未知分布场景。针对已知分布情况，我们证明了所提非贪婪求解器的收敛性（局部驻点），并通过实验表明其在刻画隐私-效用权衡方面优于现有最优方法。我们DC方法的见解同样适用于仅有标注经验样本的未知分布场景。基于这些见解，与先前基于变分推断的求解器不同，我们的交替最小化求解器满足PF的基本马尔可夫关系。实验采用MNIST和Fashion-MNIST数据集进行评估。结果表明，在相近的重构质量下，攻击者从我们压缩编码的聚类结果中获得的预测误差高于对比方法。最重要的是，与基线方法相比，我们的求解器在推理阶段独立于隐私信息。