Outsourced and Practical Privacy-Preserving K-Prototype Clustering supporting Mixed Data
Renwan Bi, Jinbo Xiong, Jie Lin, Min Zhao, Youliang Tian · 2022
Aiming to the data and model privacy issue in outsourced clustering tasks, this paper proposes an practical privacy-preserving k-prototype clustering scheme (referred to PriKPM) supporting mixed numerical and categorical attributes data. In PriKPM scheme, the users only randomly split the data sample into two shares and send them to two non-collusive servers, without interacting with the servers online. The two servers can cooperate to perform secure sample distance calculation, cluster center selection, and in-cluster sample update operations over two randomness data shares, and finally obtain the clustering distribution of samples. Specifically, we design an efficient secure comparison protocol based on additive/arithmetic secret sharing, which can switch freely between "greater than or equal" and "equal" functions, providing two comparison forms for PriKPM scheme. Theoretical analysis indicates the security and efficiency of our PriKPM scheme. Experimental results further show that compared to prior work, the clustering time of PriKPM scheme is reduced by 3 orders of magnitude.