Outsourced privacy-preserving C4.5 decision tree algorithm over horizontally and vertically partitioned dataset among multiple parties

被引:1
作者
Ye Li
Zoe L. Jiang
Lin Yao
Xuan Wang
S. M. Yiu
Zhengan Huang
机构
[1] Harbin Institute of Technology Shenzhen Graduate School,
[2] Harbin Institute of Technology Shenzhen Graduate School and Guangdong Provincial Key Laboratory of High Performance Computing,undefined
[3] Harbin Institute of Technology School of Software,undefined
[4] The University of Hong Kong,undefined
[5] Guangzhou University,undefined
来源
Cluster Computing | 2019年 / 22卷
关键词
Secure multiparty computation; Outsourced computation; C4.5 decision tree; Privacy preserving data mining; PPWAP; SSIP;
D O I
暂无
中图分类号
学科分类号
摘要
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.
引用
收藏
页码:1581 / 1593
页数:12
相关论文
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