发表机构
Korea University（高丽大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对无先验知识下LDP数值数据收集的域选择困境，提出自适应LDP框架及ABC方法，可迭代调整域适配数据分布，提升数据收集质量且对超参数鲁棒。
AI 中文摘要
本地差分隐私（Local Differential Privacy，LDP）为数值数据的收集提供了强隐私保障。然而，现有LDP机制需要预先定义数据域，而这在实际场景中往往是未知的，由此产生了关键困境：若所选域过窄，超出范围的数值会被截断，导致信息损失；反之，若域过宽，私有化过程中会添加过多噪声，降低收集数据的质量。这凸显了对可动态估计数据域的方法的需求。本研究提出一种自适应LDP框架以解决该问题：每个用户发送两类信息，即其被扰动的数值数据，以及指示其原始值是否被当前域截断的私有化信号。通过聚合这些信号，所提出的自适应截断区域边界（Adaptive Bounding of Clipping regions，ABC）方法可在无先验知识的情况下迭代调整域以适配底层数据分布。理论分析表明，估计的数据域会收敛至合适范围；实证评估结果显示，该框架在各类数据集及底层LDP机制下均显著提升了数值数据收集的质量，且估计范围在实际中成功收敛，通过全面的 ablation 研究还验证了该方法对超参数具有鲁棒性。
英文摘要
Local Differential Privacy (LDP) provides strong privacy guarantees for collecting numerical data. A fundamental challenge, however, is that existing LDP mechanisms require a predefined data domain, which is often unknown in practice. This lack of prior knowledge creates a critical dilemma for the data collector: if the chosen domain is too narrow, values outside the range are clipped, leading to information loss. Conversely, if the domain is too wide, excessive noise is added during the privatization process, which degrades the quality of collected data. This highlights the need for methods that can dynamically estimate the data domain. In this work, we propose an adaptive LDP framework that addresses this problem. In our method, each user sends two pieces of information: their perturbed numerical data, and a privatized signal indicating if their original value was clipped by the current domain. By aggregating these signals, our proposed method, Adaptive Bounding of Clipping regions (ABC) method, iteratively adjusts the domain to fit the underlying data distribution without prior knowledge. Our theoretical analysis shows that the estimated data domain converges to an appropriate range. In the empirical evaluation, the results demonstrate that our framework significantly improves the quality of numerical data collection across various datasets and underlying LDP mechanisms. We also show that the estimated range successfully converges in practice and our approach is robust to its hyperparameters through comprehensive ablation studies.
CommentsAccepted at IEEE ICDE 2026
