AI 中文总结
针对现有CPL分析方案的局限，提出依赖三元组（DT）度量，其为常数时间保守估计量，可稳健估计成对CPL，经实验验证适用于合成与真实数据集。
AI 中文摘要
收集多维用户数据是从各类应用中提取丰富洞见的基础，局部差分隐私（Local Differential Privacy，LDP）已成为缓解此类场景中隐私风险的事实标准。隐私保护型多维数据收集的核心挑战在于属性间的依赖关系，它们可能会无意中泄露相关信息并增加隐私漏洞，因此准确测量由相关性引发的隐私泄露（correlation-induced privacy leakage，CPL）对隐私分析和隐私-效用权衡至关重要。然而，现有的CPL分析方案要么需要准确的先验知识，要么在处理大量属性和高基数属性时面临可扩展性问题，这些限制了它们在真实数据中的实际应用。为解决这一研究缺口，我们提出了一种新的度量“依赖三元组”（Dependency Triad，DT），它用三个参数总结与CPL相关的成对依赖信息，并能生成成对CPL的常数时间保守估计量。DT通过其参数显式建模先验分布知识的不确定性，提供稳健的泄露估计；此外，它对稀疏分布的稳健性使其特别适用于高基数属性，而成对形式则可作为评估多维场景中总泄露的易处理构建块。在合成数据集和真实数据集上的大量实验表明，DT在不同依赖场景和先验不确定性下均能一致地估计CPL。
英文摘要
Collecting multidimensional user data is essential for extracting rich insights across various applications. Local Differential Privacy (LDP) has emerged as a de facto standard for mitigating privacy risks in such scenarios. A key challenge in privacy-preserving multidimensional data collection lies in inter-attribute dependencies, as they can inadvertently reveal correlated information and increase privacy vulnerabilities. Therefore, accurately measuring correlation-induced privacy leakage (CPL) is essential for privacy analysis and privacy-utility trade-off. However, existing CPL analysis solutions either require accurate prior knowledge or face scalability challenges for large numbers of attributes and high-cardinality attributes. These limit their practical applicability in real data. To address this research gap, we propose a novel metric, ``Dependency Triad'' (DT), which summarizes the pairwise dependency information relevant to CPL using three parameters and yields a \emph{constant-time} conservative estimator of pairwise CPL. DT explicitly models uncertainty in prior distributional knowledge through its parameters, delivering robust leakage estimates. Moreover, its robustness to sparse distributions makes it particularly suitable for high-cardinality attributes, while the pairwise formulation serves as a tractable building block for assessing total leakage in multidimensional settings. Extensive experiments on both synthetic and real datasets demonstrate that DT consistently estimates CPL across diverse dependency regimes and prior uncertainties.
CommentsAccepted for publication at the 2026 ACM SIGSAC Conference on Computer and Communications Security (ACM CCS 2026)
