论文标题
嫁接拉普拉斯和高斯分布：一种新的差异隐私噪声机制
Grafting Laplace and Gaussian distributions: A new noise mechanism for differential privacy
论文作者
论文摘要
差异隐私的框架可以保护个人的隐私，同时发布有关聚集数据的查询响应。在这项工作中，引入了一种新的噪声添加机制，以从类似于中心的拉普拉斯的混合密度来采样噪声，并在尾部中的高斯（Gaussian）采样。该密度具有更清晰的中心和轻度尾部的尾巴，具有两种分布的最佳特征。我们从理论上分析了提出的机制，并在一个维度上得出了必要和足够的条件，并在高维度下有足够的条件来保证该机制（$ε$，$δ$） - 差异隐私。与其他现有机制相比，数值模拟证实了所提出的机制的功效，从而在隐私和准确性之间取得了更好的权衡。
The framework of differential privacy protects an individual's privacy while publishing query responses on congregated data. In this work, a new noise addition mechanism for differential privacy is introduced where the noise added is sampled from a hybrid density that resembles Laplace in the centre and Gaussian in the tail. With a sharper centre and light, sub-Gaussian tail, this density has the best characteristics of both distributions. We theoretically analyze the proposed mechanism, and we derive the necessary and sufficient condition in one dimension and a sufficient condition in high dimensions for the mechanism to guarantee ($ε$,$δ$)-differential privacy. Numerical simulations corroborate the efficacy of the proposed mechanism compared to other existing mechanisms in achieving a better trade-off between privacy and accuracy.
