We study the differentially private (DP) k-means and k-median clustering problems of n points in d-dimensional Euclidean space in the massively parallel ...
Abstract. We study the differentially private (DP) k -means and k -median clustering problems of n points in d -dimensional Euclidean space in the massively ...
Oct 31, 2022 · The paper gives new algorithms for k-means and k-median clustering with differential privacy in the massively parallel computation (MPC) model.
We study the differentially private (DP) k-means and k-median clustering problems of n points in d-dimensional Euclidean space in the massively parallel ...
Apr 3, 2024 · We study the differentially private (DP) k-means and k-median clustering problems of n points in d-dimensional Euclidean space in the massively parallel ...
Nov 28, 2022 · We study the differentially private (DP) k-means and k-median clustering problems of n points in d-dimensional Euclidean space in the massively ...
Nov 1, 2023 · In this paper, we give the first algorithm that achieves near-optimal (1+\varepsilon)-approximation to (k,z)-clustering in the sliding window model.
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Locally Private k-Means Clustering with Constant Multiplicative Approximation and Near-Optimal Additive Error · Computer Science, Mathematics. AAAI · 2022.
Oct 28, 2024 · Scalable Differentially Private Clustering via Hierarchically Separated Trees. ... Near-Optimal Private and Scalable $k$-Clustering. NeurIPS 2022.
Near-Optimal Private and Scalable $k$-Clustering. Joint work with Alessandro Epasto, Vahab Mirrokni, Shyam Narayanan, and Peilin Zhong. Proceedings of the ...