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

IF 7.3 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Science China Information Sciences Pub Date : 2024-07-23 DOI:10.1007/s11432-023-4013-x
Yiwen Tong, Qi Feng, Min Luo, Debiao He
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Abstract

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.

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有特权方参与的多方隐私保护决策树训练
目前,决策树是分类任务中最常用的数据挖掘算法。虽然有大量研究对保护隐私的决策树进行了调查,但这些研究中提出的方法往往在数据隐私泄露或效率方面存在缺陷。此外,这些方法通常只适用于对称框架,即由具有同等权限的两方或多方组成,而不适用于各方权限不平等的非对称场景。在本文中,我们提出了 SecureCART,这是一种有特权方的三方隐私保护决策树训练方案。我们采用了现有的 pMPL 框架,并为除法、比较和非对称乘法设计了新颖的安全交互协议。与类似方案相比,我们的除法协议快 93.5-560.4 倍,通信开销减少 90% 以上;此外,我们的乘法协议快约 1.5 倍,通信开销减少约 20%。我们基于函数秘密共享的比较协议在适用于 pMPL 时保持了良好的性能。基于所提出的安全协议,我们用 C++ 实现了 SecureCART,并使用局域网和广域网环境中的三个真实数据集分析了其性能。实验结果表明,SecureCART 比过去研究中提出的类似方案快得多,而且使用 SecureCART 时的精度损失仍在可接受的范围内。
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来源期刊
Science China Information Sciences
Science China Information Sciences COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
12.60
自引率
5.70%
发文量
224
审稿时长
8.3 months
期刊介绍: Science China Information Sciences is a dedicated journal that showcases high-quality, original research across various domains of information sciences. It encompasses Computer Science & Technologies, Control Science & Engineering, Information & Communication Engineering, Microelectronics & Solid-State Electronics, and Quantum Information, providing a platform for the dissemination of significant contributions in these fields.
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