提出SLDP框架，让隐私保护下的数据密度自适应分析更高效。
SLDP: Semi-Local Differential Privacy for Density-Adaptive Analytics
- 根据局部密度为用户分配隐私区域，动态定义邻近关系
- 实现高分辨率网格估计，迭代次数不增加隐私开销
- 适合需要精细隐私控制的实时数据分析场景
密度自适应的域离散化对高效隐私保护分析至关重要，但在本地差分隐私（LDP）下因迭代精炼带来的隐私预算消耗而难以实现。本文提出一种新框架——半本地差分隐私（SLDP），依据局部密度为每个用户分配隐私区域，并以点在其隐私区域内可能移动为依据定义邻近性。我们设计了一个交互式 $(\varepsilon, \delta)$-SLDP 协议，由一个诚实但好奇的服务器通过公共信道执行，以私密方式估计这些区域。关键在于，该框架将隐私成本与精炼迭代次数解耦，可在不额外消耗隐私预算的前提下实现高分辨率网格。我们在合成及真实数据集上验证了该框架在估计任务中的有效性。
原文摘要 · Abstract (English)
Density-adaptive domain discretization is essential for high-utility privacy-preserving analytics but remains challenging under Local Differential Privacy (LDP) due to the privacy-budget costs associated with iterative refinement. We propose a novel framework, Semi-Local Differential Privacy (SLDP), that assigns a privacy region to each user based on local density and defines adjacency by the potential movement of a point within its privacy region. We present an interactive $(\varepsilon, δ)$-SLDP protocol, orchestrated by an honest-but-curious server over a public channel, to estimate these regions privately. Crucially, our framework decouples the privacy cost from the number of refinement iterations, allowing for high-resolution grids without additional privacy budget cost. We experimentally demonstrate the framework's effectiveness on estimation tasks across synthetic and real-world datasets.
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