Multi-party privacy-preserving decision tree training with a privileged party  

作　　者：Yiwen TONG Qi FENG Min LUO Debiao HE 

机构地区：[1]Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education,School of Cyber Science and Engineering,Wuhan University,Wuhan 430072,China [2]Shanghai Technology Innovation Centre of Distributed Privacy-Preserving Artificial Intelligence,Matrix Elements Technologies,Shanghai 200232,China [3]Key Laboratory of Computing Power Network and Information Security,Ministry of Education,Shandong Computer Science Center,Qilu University of Technology(Shandong Academy of Sciences),Jinan 250014,China

出　　处：《Science China(Information Sciences)》2024年第8期249-264,共16页中国科学（信息科学）（英文版）

基　　金：supported in part by Major Program(JD)of Hubei Province(Grant No.2023BAA027);National Natural Science Foundation of China(Grant Nos.62202339,62172307,U21A20466,62325209);New 20 Project of Higher Education of Jinan(Grant No.202228017);Fundamental Research Funds for the Central Universities(Grant No.2042023KF0203).

摘　　要：Currently,a decision tree is the most commonly used data mining algorithm for classification tasks.While a significant number of studies have investigated privacy-preserving decision trees,the methods proposed in these studies often have shortcomings in terms of data privacy breach or efficiency.Additionally,these methods typically only apply to symmetric frameworks,which consist of two or more parties with equal privilege,and are not suitable for asymmetric scenarios where parties have unequal privilege.In this paper,we propose SecureCART,a three-party privacy-preserving decision tree training scheme with a privileged party.We adopt the existing pMPL framework and design novel secure interactive protocols for division,comparison,and asymmetric multiplication.Compared to similar schemes,our division protocol is 93.5-560.4×faster,with the communication overhead reduced by over 90%;further,our multiplication protocol is approximately 1.5×faster,with the communication overhead reduced by around 20%.Our comparison protocol based on function secret sharing maintains good performance when adapted to pMPL.Based on the proposed secure protocols,we implement SecureCART in C++and analyze its performance using three real-world datasets in both LAN and WAN environments.he experimental results indicate that SecureCART is significantly faster than similar schemes proposed in past studies,and that the loss of accuracy while using SecureCART remains within an acceptable range.

关 键 词：privacy protection decision trees secure multi-party computation secret sharing privileged party 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论] TP309[自动化与计算机技术—计算机科学与技术]