FriendlyCore: Practical Differentially Private Aggregation

• URL: http://arxiv.org/abs/2110.10132v1
• Date: Tue, 19 Oct 2021 17:43:50 GMT
• Title: FriendlyCore: Practical Differentially Private Aggregation
• Authors: Eliad Tsfadia, Edith Cohen, Haim Kaplan, Yishay Mansour, Uri Stemmer
• Abstract summary: We propose a simple and practical tool $mathsfFriendlyCore$ that takes a set of points $cal D$ from an unrestricted (pseudo) metric space as input. When $cal D$ has effective diameter $r$, $mathsfFriendlyCore$ returns a "stable" subset $cal D_Gsubseteq cal D$ that includes all points. $mathsfFriendlyCore$ can be used to preprocess the input before privately aggregating it, potentially simplifying the aggregation or boosting its accuracy
• Score: 67.04951703461657
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Differentially private algorithms for common metric aggregation tasks, such as clustering or averaging, often have limited practicality due to their complexity or a large number of data points that is required for accurate results. We propose a simple and practical tool $\mathsf{FriendlyCore}$ that takes a set of points ${\cal D}$ from an unrestricted (pseudo) metric space as input. When ${\cal D}$ has effective diameter $r$, $\mathsf{FriendlyCore}$ returns a "stable" subset ${\cal D}_G\subseteq {\cal D}$ that includes all points, except possibly few outliers, and is {\em certified} to have diameter $r$. $\mathsf{FriendlyCore}$ can be used to preprocess the input before privately aggregating it, potentially simplifying the aggregation or boosting its accuracy. Surprisingly, $\mathsf{FriendlyCore}$ is light-weight with no dependence on the dimension. We empirically demonstrate its advantages in boosting the accuracy of mean estimation, outperforming tailored methods.

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