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.

翻译：本研究利用信息论中的隐私漏斗（PF）模型，开发了一种通过端到端训练框架实现隐私保护表征学习的方法。我们严格权衡了混淆性与实用性之间的矛盾，两者均通过对数损失（亦称为自信息损失）进行量化。这一探索深化了信息论隐私与表征学习之间的相互作用，为判别模型与生成模型的数据保护机制提供了实质性见解。重要的是，我们将该模型应用于最先进的人脸识别系统。该模型展现出对多种输入（从原始人脸图像到衍生或精炼的嵌入特征）的适应能力，并能胜任分类、重建及生成等任务。