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：我们提出了一种基于隐私漏斗（PF）方法差分凸（DC）结构的高效求解器。所提出的DC分解可导出闭式更新方程，从而能够直接适用于已知与未知分布场景。在已知分布情况下，我们证明了该非贪婪求解器的收敛性（局部平稳点），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。我们的DC方法中的洞见同样适用于仅有带标签经验样本的未知分布场景。基于这些洞见，与先前基于变分推理的求解器不同，我们提出的交替最小化求解器满足PF的基本马尔可夫关系。在实验层面，我们使用MNIST和Fashion-MNIST数据集评估了所提求解器。结果表明，在可比较的重建质量下，攻击者通过聚类我们压缩编码得到的预测误差高于对比方法。最重要的是，与基线方法不同，我们的求解器在推理阶段与隐私信息无关。