系统梳理差分隐私审计方法，提出高效、端到端、精确三大标准。
The Hitchhiker's Guide to Efficient, End-to-End, and Tight DP Auditing
- 构建统一框架，归纳现有审计技术的运作模式与评估方式。
- 揭示当前方法在效率、完整性和精度上的瓶颈与局限。
- 为研究者提供可复用的评估工具，助力突破隐私保护关键难题。
本文系统化研究差分隐私（DP）审计技术，旨在提炼核心洞见并识别开放挑战。首先，提出一个全面的综述框架，并确立三项跨场景应追求的目标：效率、端到端性与紧致性。随后，对前沿DP审计技术的运行模式进行归类，涵盖威胁模型、攻击方式与评估函数。该分析揭示了以往研究忽略的关键细节，剖析实现三大目标的制约因素，并指出未解的研究问题。整体上，本工作提供了一种可复用的系统性方法论，用于评估领域进展，识别痛点与未来研究方向。
原文摘要 · Abstract (English)
In this paper, we systematize research on auditing Differential Privacy (DP) techniques, aiming to identify key insights and open challenges. First, we introduce a comprehensive framework for reviewing work in the field and establish three cross-contextual desiderata that DP audits should target -- namely, efficiency, end-to-end-ness, and tightness. Then, we systematize the modes of operation of state-of-the-art DP auditing techniques, including threat models, attacks, and evaluation functions. This allows us to highlight key details overlooked by prior work, analyze the limiting factors to achieving the three desiderata, and identify open research problems. Overall, our work provides a reusable and systematic methodology geared to assess progress in the field and identify friction points and future directions for our community to focus on.
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