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Locally Private k-Means in One Round

Alisa Chang
Ravi Kumar Ravikumar
International Conference on Machine Learning (ICML) (2021), pp. 1441-1451
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We study k-means clustering in the non-interactive (aka one-round) local model of differential privacy. We give an approximation algorithm that requires a single round of communication and achieves an approximation ratio arbitrarily close to the best non private approximation algorithm. To show the flexibility of our framework, we also demonstrate that it yields a similar near-optimal approximation algorithm in the (one-round) shuffle model.