Locally Private k-Means Clustering

4 July 2019

Papers citing "Locally Private k-Means Clustering"

10 / 10 papers shown

Title
FastLloyd: Federated, Accurate, Secure, and Tunable $k$ -Means Clustering with Differential Privacy Abdulrahman Diaa Thomas Humphries Florian Kerschbaum FedML 41 0 0 03 May 2024
Differentially Private Aggregation via Imperfect Shuffling Badih Ghazi Ravi Kumar Pasin Manurangsi Jelani Nelson Samson Zhou FedML 35 1 0 28 Aug 2023
Certified private data release for sparse Lipschitz functions Konstantin Donhauser J. Lokna Amartya Sanyal M. Boedihardjo R. Honig Fanny Yang 48 3 0 19 Feb 2023
Differentially-Private Clustering of Easy Instances E. Cohen Haim Kaplan Yishay Mansour Uri Stemmer Eliad Tsfadia 29 22 0 29 Dec 2021
Tight and Robust Private Mean Estimation with Few Users Cheng-Han Chiang Vahab Mirrokni Hung-yi Lee FedML 31 28 0 22 Oct 2021
Differentially Private Aggregation in the Shuffle Model: Almost Central Accuracy in Almost a Single Message Badih Ghazi Ravi Kumar Pasin Manurangsi Rasmus Pagh Amer Sinha FedML 67 36 0 27 Sep 2021
Differentially Private Algorithms for Clustering with Stability Assumptions M. Shechner 27 2 0 11 Jun 2021
Private Counting from Anonymous Messages: Near-Optimal Accuracy with Vanishing Communication Overhead Badih Ghazi Ravi Kumar Pasin Manurangsi Rasmus Pagh FedML 37 48 0 08 Jun 2021
Utility-efficient Differentially Private K-means Clustering based on Cluster Merging Tianjiao Ni Minghao Qiao Zhili Chen Shun Zhang Hong Zhong FedML 6 30 0 03 Oct 2020
The power of synergy in differential privacy: Combining a small curator with local randomizers A. Beimel Aleksandra Korolova Kobbi Nissim Or Sheffet Uri Stemmer 34 14 0 18 Dec 2019