Efficient privacy-preserving decision tree classification protocol

Lichuan MA; Jiayi PENG; Qingqi PEI; Haojin ZHU

doi:10.11959/j.issn.1000-436x.2021149

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Efficient privacy-preserving decision tree classification protocol

Papers | 更新时间：2024-06-05

- Efficient privacy-preserving decision tree classification protocol
- Journal on Communications Vol. 42, Issue 8, Pages: 80-89(2021)
- 作者机构：
  
  1. 西安电子科技大学综合业务网理论及关键技术国家重点实验室，陕西西安 710071
  2. 陕西省区块链与安全计算重点实验室，陕西西安 710071
  3. 上海交通大学计算机学院，上海 200240
- 作者简介：
- 基金信息：
  
  The National Key Research and Development Program of China(2020YFB1807500);The National Natural Science Foundation of China(61902292);The National Natural Science Foundation of China(61972453);The National Natural Science Foundation of China(62072355);The Key Research and Development Programs of Shaan-xi(2021ZDLGY06-03);The Fundamental Research Funds for the Central Univer-sities(XJS201502)
- DOI：10.11959/j.issn.1000-436x.2021149
  CLC： TN92
- Online First：2021-08，
  
  Published：25 August 2021
- 稿件说明：
移动端阅览
Lichuan MA, Jiayi PENG, Qingqi PEI, et al. Efficient privacy-preserving decision tree classification protocol[J]. Journal on Communications, 2021, 42(8): 80-89.
DOI：

Lichuan MA, Jiayi PENG, Qingqi PEI, et al. Efficient privacy-preserving decision tree classification protocol[J]. Journal on Communications, 2021, 42(8): 80-89. DOI： 10.11959/j.issn.1000-436x.2021149.

摘要

为了有效解决物联网大数据场景中的决策树隐私分类服务问题，将决策树分类模型与安全多方计算技术相结合，提出了一种高效的决策树隐私分类服务协议。该协议包括：决策树分类模型混淆、基于布尔共享的隐私比较和基于不经意传输的隐私分类结果获取3个阶段。该协议能够同时保护服务提供商决策树分类模型参数及结构特征和用户需要进行分类的特征数据不被泄露。安全性分析表明，所提决策树隐私分类服务协议能够抵抗“诚实好奇”的攻击者。将所提协议用于通过公开数据集得到的决策树分类模型，以分类准确率和完成隐私分类服务的时间效率为指标与现有方法进行对比，实验结果验证了所提出隐私分类服务协议的准确性和高效性。

Abstract

To provide privacy-preserving decision tree classification services in the Internet of things (IoT) big data scenario

an efficient privacy-preserving decision tree classification protocol was proposed by adopting the secure multiparty computation framework into the classification model.The entire protocol consisted of three parts: the original decision tree model mixing

the Boolean share-based privacy-preserving comparing

and the 1-out-of-n oblivious transfer-based classification result obtaining.Via the proposed protocol

the service providers could protect the parameters of their decision tree models and the users were able to derive the classification result without exposing their privately hold data.Through a concrete security analysis

the proposed protocol was proved to be secure against semi-honest adversaries.By implementing the proposed protocol on various practical decision tree models from open datasets

the classification accuracy and the average time cost for completing one privacy-preserving classification service were evaluated.After compared with existing related works

the performance superiority of the proposed protocol is demonstrated.

关键词

Keywords

references

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