A High-Availability K-modes Clustering Method Based on Differential Privacy

In categorical data mining, the K-modes algorithm is a classic algorithm that has been widely used. However, the data analyzed by the K-modes algorithm usually contains sensitive user information. If these data are leaked, it will seriously threaten the privacy of users. In response to this problem, the existing method that combines differential privacy with the K-modes algorithm can effectively prevent privacy leakage. Nevertheless, differential privacy adds noise to the data while protecting data privacy, which will reduce the availability of clustering results. In this paper, we propose a high-availability K-modes clustering mechanism based on differential privacy(HAKC). In this mechanism, based on the use of differential privacy to protect data privacy, we select the initial centroid of the clustering by calculation, and improve the calculation method of the distance between the data point and the centroid in the iterative process.