Private Distributed K-Means Clustering on Interval Data

Dingquan Huang, Xin Yao, Senquan An, Shengbing Ren

Abstract: K-means clustering has been heavily employed to mine valuable insights from interval data. Nevertheless, serious privacy leakage concerns are stumbling blocks impeding its widespread application. To quantify the privacy of small and large-scale interval data, we introduce two notions of α-Condensed Local Differential Privacy and ϵ-Local Differential Privacy, and propose two distance-aware perturbation mechanisms of α-exponential and square wave mechanisms. Rigorous theoretical analysis proves that our proposed mechanisms satisfy these two privacy notions. The experimental results built on multiple synthesized and real datasets show that our proposed mechanisms can provide more accurate clustering results than prior work, such as Randomized Response, Generalized Randomized Response, and Optimized Local Hash.