发表机构
University of North Texas（北德克萨斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对多属性记录的异构性，提出依赖感知的异构数据扰动框架DepHDP，通过属性分组和混合机制选择，在全局ℓ_p度量mDP约束下联合优化隐私预算与机制设计，以更低计算成本改善隐私-效用权衡。
AI 中文摘要
多属性用户记录本质上是异构的，通常包含连续、分类和二元属性，并且经常表现出强烈的跨属性依赖关系。为此类记录设计高实用性的度量差分隐私（mDP）机制具有挑战性。简单的预定义机制（如基于距离的噪声）可能与特定任务的效用损失对齐不佳，而完全基于优化的机制对于多属性记录而言可能计算上不可行。我们提出了依赖感知的异构数据扰动（DepHDP）框架，用于多属性mDP，该框架将依赖感知的属性分组与异构扰动设计相结合。DepHDP并非采用一刀切的机制，而是为每个属性或属性组选择合适的扰动策略，在高效的预定义机制（如拉普拉斯机制或指数机制）与基于优化的设计之间进行选择。该选择由领域大小和预定义噪声充分性准则共同指导，该准则量化了任务引起的效用损失是否能由扰动幅度很好地解释。为了支持可扩展的端到端优化，DepHDP通过采样和轻量级代理建模来估计组级效用损失，并在全局ℓ_p度量mDP约束下联合优化隐私预算分配和组级机制设计。在三个案例研究中，DepHDP在评估领域上以低于全记录OPT的计算成本，相比均匀基线改善了隐私-效用权衡。
英文摘要
Multi-attribute user records are inherently heterogeneous, often combining continuous, categorical, and binary attributes, and they frequently exhibit strong cross-attribute dependencies. Designing high-utility metric differential privacy (mDP) mechanisms for such records is challenging. Simple predefined mechanisms, such as distance-based noise, may be poorly aligned with task-specific utility loss, whereas fully optimization-based mechanisms can be computationally prohibitive for multi-attribute records. We propose Dependency-aware Heterogeneous Data Perturbation (DepHDP)}, a framework for multi-attribute mDP that combines dependency-aware attribute grouping with heterogeneous perturbation design. Rather than applying a one-size-fits-all mechanism, DepHDP selects an appropriate perturbation strategy for each attribute or attribute group, choosing between efficient predefined mechanisms, such as Laplace or Exponential mechanisms, and optimization-based designs. This selection is guided by both domain size and a predefined-noise adequacy criterion, which quantifies whether task-induced utility loss can be well explained by perturbation magnitude. To support scalable end-to-end optimization, DepHDP estimates group-level utility loss through sampling and lightweight surrogate modeling, and jointly optimizes privacy-budget allocation and group-wise mechanism design under a global $\ell_p$-metric mDP constraint. Across three case studies, DepHDP improves privacy--utility trade-offs over uniform baselines at lower computational cost than full-record OPT on evaluated domains.
CommentsIEEE Symposium on Security and Privacy (S&P), 2027
