Secure and Privacy-Preserving Decision Tree Classification with Lower Complexity
作者机构：Department of Electrical and Computer EngineeringUniversity of WaterlooWaterloo N2L 3G1Canada School of Computer ScienceUniversity of GuelphGuelph N1G 2W1Canada Department of Electrical and Computer EngineeringUniversity of WaterlooWaterloo N2L 3G1Canada
出 版 物：《Journal of Communications and Information Networks》 (通信与信息网络学报（英文）)
年 卷 期：2020年第5卷第1期
页 面：16-25页
核心收录：
学科分类：0839[工学-网络空间安全] 08[工学] 081201[工学-计算机系统结构] 0812[工学-计算机科学与技术（可授工学、理学学位）]
主 题：decision trees data privacy model privacy secure comparison machine-learning classification
摘 要：As a widely-used machine-learning classifier,a decision tree model can be trained and deployed at a service provider to provide classification services for clients,e.g.,remote *** address privacy concerns regarding the sensitive information in these services(i.e.,the clients’inputs,model parameters,and classification results),we propose a privacy-preserving decision tree classification scheme(PDTC)in this ***,we first tailor an additively homomorphic encryption primitive and a secret sharing technique to design a new secure two-party comparison protocol,where the numeric inputs of each party can be privately compared as a whole instead of doing that in a bit-by-bit ***,based on the comparison protocol,we exploit the structure of the decision tree to construct PDTC,where the input of a client and the model parameters of a service provider are concealed from the counterparty and the classification result is only revealed to the client.A formal simulation-based security model and the security proof demonstrate that PDTC achieves desirable security *** addition,performance evaluation shows that PDTC achieves a lower communication and computation overhead compared with existing schemes.
维普期刊数据库