Outsourced privacy-preserving C4.5 decision tree algorithm over horizontally and vertically partitioned dataset among multiple parties
Outsourced privacy-preserving C4.5 decision tree algorithm over horizontally and vertically partitioned dataset among multiple parties
复制标题
在多方之间水平和垂直划分的数据集上外包隐私保护 C4.5 决策树算法
DOI：
10.1007/s10586-017-1019-9
复制
发表时间：
2019-01-01
影响因子：
4.4
通讯作者：
Huang, Zhengan
中科院分区：
文献类型：
--
作者：
Li, Ye;Jiang, Zoe L.;Huang, Zhengan
许多公司希望为数据挖掘任务共享数据。然而，隐私和安全方面的担忧已经成为数据共享领域的瓶颈。基于安全多方计算(SMC)的隐私保护数据挖掘为解决这一问题而应运而生。然而，在传统的SMC解决方案中，用户端的计算代价很高。本研究引入外包的方式来降低用户端的计算成本。我们还基于外包隐私保护加权平均协议(OPPWAP)和外包安全集交集协议(Ossip)，提出了一种面向多方的水平和垂直分割数据的外包隐私保护C4.5算法。因此，我们发现我们的方法可以在保持数据隐私的情况下获得与原始C4.5决策树算法相同的结果。此外，我们还实现了所提出的协议和算法。结果表明，用户侧的计算成本与参与方的数量之间存在次线性关系。
Many companies want to share data for data-mining tasks. However, privacy and security concerns have become a bottleneck in the data-sharing field. The secure multiparty computation (SMC)-based privacy-preserving data mining has emerged as a solution to this problem. However, there is heavy computation cost at user side in traditional SMC solutions. This study introduces an outsourcing method to reduce the computation cost of the user side. We also preserve the privacy of the shared databy proposing an outsourced privacy-preserving C4.5 algorithm over horizontally and vertically partitioned data for multiple parties based on the outsourced privacy preserving weighted average protocol (OPPWAP) and outsourced secure set intersection protocol (OSSIP). Consequently, we have found that our method can achieve a result same the original C4.5 decision tree algorithm while preserving data privacy. Furthermore, we also implement the proposed protocols and the algorithms. It shows that a sublinear relationship exists between the computational cost of the user side and the number of participating parties.