Private Clustering

Private clustering aims to group data points into clusters while preserving the privacy of individual data points, typically using differential privacy mechanisms. Current research focuses on developing efficient algorithms for various clustering objectives (e.g., k-means, k-median) under different privacy models (centralized, local, federated), adapting to data streams and continual observation settings, and improving clustering quality while maintaining strong privacy guarantees. This field is significant because it enables the analysis of sensitive data in applications like personalized recommendations and fraud detection, where privacy is paramount, without sacrificing the utility of clustering results.