FriendlyCore: Practical Differentially Private Aggregation
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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 𝖥𝗋𝗂𝖾𝗇𝖽𝗅𝗒𝖢𝗈𝗋𝖾 that takes a set of points D from an unrestricted (pseudo) metric space as input. When D has effective diameter r, 𝖥𝗋𝗂𝖾𝗇𝖽𝗅𝗒𝖢𝗈𝗋𝖾 returns a "stable" subset D_G⊆ D that includes all points, except possibly few outliers, and is certified to have diameter r. 𝖥𝗋𝗂𝖾𝗇𝖽𝗅𝗒𝖢𝗈𝗋𝖾 can be used to preprocess the input before privately aggregating it, potentially simplifying the aggregation or boosting its accuracy. Surprisingly, 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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