In this study, we apply the information-theoretic Privacy Funnel (PF) model
to the domain of face recognition, developing a novel method for
privacy-preserving representation learning within an end-to-end training
framework. Our approach addresses the trade-off between obfuscation and utility
in data protection, quantified through logarithmic loss, also known as
self-information loss. This research provides a foundational exploration into
the integration of information-theoretic privacy principles with representation
learning, focusing specifically on the face recognition systems. We
particularly highlight the adaptability of our framework with recent
advancements in face recognition networks, such as AdaFace and ArcFace. In
addition, we introduce the Generative Privacy Funnel ($\mathsf{GenPF}$) model,
a paradigm that extends beyond the traditional scope of the PF model, referred
to as the Discriminative Privacy Funnel ($\mathsf{DisPF}$). This
$\mathsf{GenPF}$ model brings new perspectives on data generation methods with
estimation-theoretic and information-theoretic privacy guarantees.
Complementing these developments, we also present the deep variational PF
(DVPF) model. This model proposes a tractable variational bound for measuring
information leakage, enhancing the understanding of privacy preservation
challenges in deep representation learning. The DVPF model, associated with
both $\mathsf{DisPF}$ and $\mathsf{GenPF}$ models, sheds light on connections
with various generative models such as Variational Autoencoders (VAEs),
Generative Adversarial Networks (GANs), and Diffusion models. Complementing our
theoretical contributions, we release a reproducible PyTorch package,
facilitating further exploration and application of these privacy-preserving
methodologies in face recognition systems.

该研究应用信息论隐私漏斗（PF）模型于人脸识别领域，在端到端训练框架中开发了一种新的隐私保护表征学习方法，解决了数据保护中混淆和效用之间的权衡问题。

深度隐私漏斗模型：从判别到生成方法，应用于人脸识别

Deep Privacy Funnel Model: From a Discriminative to a Generative  Approach with an Application to Face Recognition

In this study, we harness the information-theoretic Privacy Funnel (PF) model
to develop a method for privacy-preserving representation learning using an
end-to-end training framework. We rigorously address the trade-off between
obfuscation and utility. Both are quantified through the logarithmic loss, a
measure also recognized as self-information loss. This exploration deepens the
interplay between information-theoretic privacy and representation learning,
offering substantive insights into data protection mechanisms for both
discriminative and generative models. Importantly, we apply our model to
state-of-the-art face recognition systems. The model demonstrates adaptability
across diverse inputs, from raw facial images to both derived or refined
embeddings, and is competent in tasks such as classification, reconstruction,
and generation.

利用信息论隐私漏斗（PF）模型，开发出一种使用端到端训练框架的隐私保存表示学习方法，通过对抹消和实用性之间的权衡进行量化来实现。我们将其应用于最先进的人脸识别系统，该模型展示了在不同输入（从原始面部图像到派生或改进的嵌入）上的适应性，并且在分类、重建和生成等任务中表现出了竞争力。