系统梳理生成模型的隐私与效用平衡，为研究者提供清晰框架。
Privacy-Preserving Generative Models: A Comprehensive Survey
- 基于100篇论文构建隐私与效用的分类体系
- 首次系统整合GANs与VAEs在隐私保护上的评估维度
- 适合刚入该领域的研究者快速掌握核心概念
尽管生成模型取得了突破性进展，但其对隐私与效用的影响日益引发关注。虽然已有研究揭示了GAN带来的隐私风险，但尚无综述系统分类生成模型在隐私与效用方面的研究视角。本文通过分析100篇相关论文，全面研究隐私保护生成模型，提出全新的隐私与效用度量分类体系。最后，讨论当前挑战与未来研究方向，帮助新研究者深入理解核心概念。
原文摘要 · Abstract (English)
Despite the generative model's groundbreaking success, the need to study its implications for privacy and utility becomes more urgent. Although many studies have demonstrated the privacy threats brought by GANs, no existing survey has systematically categorized the privacy and utility perspectives of GANs and VAEs. In this article, we comprehensively study privacy-preserving generative models, articulating the novel taxonomies for both privacy and utility metrics by analyzing 100 research publications. Finally, we discuss the current challenges and future research directions that help new researchers gain insight into the underlying concepts.
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