通过相关噪声设计，本地隐私机制可逼近中心化最优精度。
Local Differential Privacy with Correlated Noise Achieves Central-DP Optimal Cost
- 用相关噪声替代独立噪声，优化本地差分隐私机制
- 在局部添加噪声后，估计误差逼近中心化最优水平
- 适合关注隐私与精度平衡的研究者和系统设计者
研究在诚实但好奇的服务器环境下，对用户持有的 $n$ 个数值之和进行私密估计。为确保数据发布及服务器计算全过程的隐私性，采用本地（纯）差分隐私模型，即每位用户传输经过噪声扰动的值。传统上，本地独立噪声会带来显著效用损失，相比仅在聚合后加噪的中心化模型。本文证明该差距并非本质存在：通过精心设计本地添加噪声变量之间的相关性，构造出 $\varepsilon$-DP 机制，其估计成本可逼近中心化设定下的最优成本，误差任意小。
原文摘要 · Abstract (English)
We study privately estimating the sum of $n$ user-held values in the presence of an honest-but-curious server. This motivates requiring privacy not only at data release but also throughout server-side computation. We therefore adopt the local (pure) differential privacy model, in which each user transmits a noise-perturbed value. It is well known that independent local noise typically incurs a substantial utility loss compared to the centralized model, where noise is added only after aggregation. We show that this gap is not fundamental. By carefully designing correlations among the locally added noise variables, we construct $\varepsilon$-DP mechanisms whose estimation cost matches the optimal cost achievable in the centralized setting, up to an arbitrarily small error.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。