Improving the Utility of Differentially Private Clustering through Dynamical Processing
This addresses the problem of poor utility in private clustering for complex data distributions, though it appears incremental as it builds on existing methods.
The paper tackles the trade-off between utility and privacy in differentially private clustering, particularly for non-convex clusters, by proposing a dynamical processing method inspired by Morse theory. Experiments show it improves clustering performance at the same privacy level, with theoretical results indicating minimal additional privacy loss.
This study aims to alleviate the trade-off between utility and privacy of differentially private clustering. Existing works focus on simple methods, which show poor performance for non-convex clusters. To fit complex cluster distributions, we propose sophisticated dynamical processing inspired by Morse theory, with which we hierarchically connect the Gaussian sub-clusters obtained through existing methods. Our theoretical results imply that the proposed dynamical processing introduces little to no additional privacy loss. Experiments show that our framework can improve the clustering performance of existing methods at the same privacy level.