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Secure and Privacy-Preserving Decision Tree Classification with Lower Complexity

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文摘 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 diagnostics. To 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 paper. Specifically, 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 manner. Then, 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 properties. In addition, performance evaluation shows that PDTC achieves a lower communication and computation overhead compared with existing schemes.
来源 Journal of Communications and Information Networks ,2020,5(1):16-25 【核心库】
DOI 10.23919/JCIN.2020.9055107
关键词 decision trees ; data privacy ; model privacy ; secure comparison ; machine-learning classification
地址

1. Department of Electrical and Computer Engineering, University of Waterloo, Canada, Waterloo, N2L 3G1  

2. School of Computer Science, University of Guelph, Canada, Guelph, N1G2W1

语种 英文
文献类型 研究性论文
ISSN 2096-1081
学科 电子技术、通信技术
文献收藏号 CSCD:6681623

参考文献 共 29 共2页

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引证文献 1 篇

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