Colored Noise Mechanism for Differentially Private Clustering
This addresses privacy concerns in clustering for data analysts, but it is incremental as it builds on existing differential privacy mechanisms.
The paper tackles the problem of ensuring differential privacy for K-means clustering by proposing a mechanism that adds Gaussian noise with an optimized covariance, resulting in analytical solutions that prove efficacy compared to state-of-the-art methods.
The goal of this paper is to propose and analyze a differentially private randomized mechanism for the $K$-means query. The goal is to ensure that the information received about the cluster-centroids is differentially private. The method consists in adding Gaussian noise with an optimum covariance. The main result of the paper is the analytical solution for the optimum covariance as a function of the database. Comparisons with the state of the art prove the efficacy of our approach.