FriendlyCore: Practical Differentially Private Aggregation

Eliad Tsfadia, Edith Cohen, Haim Kaplan, Yishay Mansour, Uri Stemmer

Research output: Contribution to journalConference articlepeer-review


Differentially private algorithms for common metric aggregation tasks, such as clustering or averaging, often have limited practicality due to their complexity or to the 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 C ⊆ D that includes all points, except possibly a few outliers, and is guaranteed 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 and clustering tasks such as k-means and k-GMM, outperforming tailored methods.

Original languageEnglish
Pages (from-to)21828-21863
Number of pages36
JournalProceedings of Machine Learning Research
StatePublished - 2022
Event39th International Conference on Machine Learning, ICML 2022 - Baltimore, United States
Duration: 17 Jul 202223 Jul 2022

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Statistics and Probability


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