论文标题
高斯稀疏直方图机制的确切隐私分析
Exact Privacy Analysis of the Gaussian Sparse Histogram Mechanism
论文作者
论文摘要
稀疏的直方图方法对于在大型或无限直方图，大型逐个查询以及更一般而言，释放一组具有足够项目计数的统计数据的差异私人计数可能很有用。我们考虑了稀疏直方图机制的高斯版本，并研究了该机制所满足的确切$ε，δ$差异隐私。我们将这些精确的$ε，δ$参数与以前工作中用于量化其宽松隐私范围的影响的简单高估。
Sparse histogram methods can be useful for returning differentially private counts of items in large or infinite histograms, large group-by queries, and more generally, releasing a set of statistics with sufficient item counts. We consider the Gaussian version of the sparse histogram mechanism and study the exact $ε,δ$ differential privacy guarantees satisfied by this mechanism. We compare these exact $ε,δ$ parameters to the simpler overestimates used in prior work to quantify the impact of their looser privacy bounds.
