论文标题
私人热图
Differentially Private Heatmaps
论文作者
论文摘要
我们考虑在保护其隐私的同时，从用户的总数据中生产热图的任务。我们为此任务提供了差异性私有（DP）算法，并证明了其优于实际数据集上的先前算法。 我们的核心算法原始算法是DP过程，它采用一组分布，并产生一个接近地球移动器与输入平均值的输出。我们证明了在某个稀疏性假设下我们算法的误差的理论界限，并且这些算法几乎是最佳的。
We consider the task of producing heatmaps from users' aggregated data while protecting their privacy. We give a differentially private (DP) algorithm for this task and demonstrate its advantages over previous algorithms on real-world datasets. Our core algorithmic primitive is a DP procedure that takes in a set of distributions and produces an output that is close in Earth Mover's Distance to the average of the inputs. We prove theoretical bounds on the error of our algorithm under a certain sparsity assumption and that these are near-optimal.
