In this study, we apply the information-theoretic Privacy Funnel (PF) model to face recognition and develop a method for privacy-preserving representation learning within an end-to-end trainable framework. Our approach addresses the trade-off between utility and obfuscation of sensitive information under logarithmic loss. We study the integration of information-theoretic privacy principles with representation learning, with a particular focus on face recognition systems. We also highlight the compatibility of the proposed framework with modern face recognition networks such as AdaFace and ArcFace. In addition, we introduce the Generative Privacy Funnel ($\mathsf{GenPF}$) model, which extends the traditional discriminative PF formulation, referred to here as the Discriminative Privacy Funnel ($\mathsf{DisPF}$). The proposed $\mathsf{GenPF}$ model extends the privacy-funnel framework to generative formulations under information-theoretic and estimation-theoretic criteria. Complementing these developments, we present the deep variational PF (DVPF) model, which yields a tractable variational bound for measuring information leakage and enables optimization in deep representation-learning settings. The DVPF framework, associated with both the $\mathsf{DisPF}$ and $\mathsf{GenPF}$ models, also clarifies connections with generative models such as variational autoencoders (VAEs), generative adversarial networks (GANs), and diffusion models. Finally, we validate the framework on modern face recognition systems and show that it provides a controllable privacy--utility trade-off while substantially reducing leakage about sensitive attributes. To support reproducibility, we also release a PyTorch implementation of the proposed framework.

翻译：本研究将信息论隐私漏斗（PF）模型应用于人脸识别，并开发了一种在端到端可训练框架内实现隐私保护表示学习的方法。我们的方法解决了在对数损失下，效用与敏感信息混淆之间的权衡问题。我们研究了信息论隐私原则与表示学习的融合，特别关注人脸识别系统。同时，我们强调了所提出的框架与现代人脸识别网络（如AdaFace和ArcFace）的兼容性。此外，我们引入了生成式隐私漏斗模型，该模型扩展了传统的判别式PF公式（这里称为判别式隐私漏斗）。所提出的模型将隐私漏斗框架扩展到信息论和估计论标准下的生成式公式中。作为这些发展的补充，我们提出了深度变分隐私漏斗（DVPF）模型，该模型为衡量信息泄露提供了可处理的变分界，并能够在深度表示学习设置中进行优化。与和模型相关联的DVPF框架也阐明了其与生成式模型（如变分自编码器（VAE）、生成对抗网络（GAN）和扩散模型）的联系。最后，我们在现代人脸识别系统上验证了该框架，并表明它提供了可控的隐私-效用权衡，同时大幅减少了敏感属性的泄露。为了支持可重复性，我们还发布了所提出框架的PyTorch实现。