Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (11A): 241200115-9.doi: 10.11896/jsjkx.241200115

• Information Security • Previous Articles     Next Articles

Design and Application of Decision Tree Algorithms for Privacy-preserving

LI Jincheng, LI Yingna, FU Guoqing   

  1. School of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650500,China
    Yunnan Provincial Key Laboratory of Computer Technology Application,Kunming 650500,China
  • Online:2025-11-15 Published:2025-11-10
  • Supported by:
    Major Science and Technology Project of Yunnan(202302AD080002,202402AD080003)．

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Abstract: In the digital era,data has emerged as a critical asset.Data sharing not only fuels advancements in the artificial intelligence sector,but also poses the threat of privacy violations.Fully Homomorphic Encryption(FHE) technology offers a secure solution for executing various machine learning algorithms on encrypted data,bypassing the risks associated with data exposure.Nonetheless,operations on encrypted data demand a significant computational overhead,prompting the need for algorithms to be redesigned with FHE optimization in mind.This paper introduces a novel privacy-preserving decision tree scheme based on the CKKS fully homomorphic encryption algorithm.It utilizes a low-degree approximate step function and a lightweight interaction protocol to supplant complex nonlinear operations,enabling the training and inference of decision trees directly on encrypted data.Extensive experiments on four benchmark UCI datasets reveal that the proposed scheme achieves an average AUC of 0.92 and an average F1-Score of 0.77,outperforming both the PrivaTree and SecDT schemes while also exhibiting greater stability.

Key words: Fully homomorphic encryption, Privacy-preserving, Decision tree

CLC Number: 

  • TP311
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