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

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Abstract

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.

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Acknowledgements

This work is supported by National High Technology Research and Development Program of China (No. 2015AA016008), National Natural Science Foundation of China (No. 61402136), Natural Science Foundation of Guangdong Province, China (No. 2014A030313697), National Natural Science Foundation of China (No. 61472091), Natural Science Foundation of Guangdong Province for Distinguished Young Scholars (2014A030306020), Guangzhou scholars project for universities of Guangzhou (No. 1201561613), Science and Technology Planning Project of Guangdong Province, China (2015B01012 9015) and Guangdong Province Key Laboratory of High Performance Computing (No. [2013]82).

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Authors and Affiliations

  1. Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, 518055, China

    Ye Li & Xuan Wang

  2. Harbin Institute of Technology Shenzhen Graduate School and Guangdong Provincial Key Laboratory of High Performance Computing, Shenzhen, 518055, China

    Zoe L. Jiang

  3. Harbin Institute of Technology School of Software, Harbin, 150001, China

    Lin Yao

  4. The University of Hong Kong, Hong Kong, SAR, China

    S. M. Yiu

  5. Guangzhou University, Guangzhou, 510006, China

    Zhengan Huang

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  1. Ye Li
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  2. Zoe L. Jiang
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  3. Lin Yao
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  4. Xuan Wang
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  5. S. M. Yiu
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Correspondence to Ye Li or Zoe L. Jiang.

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Li, Y., Jiang, Z.L., Yao, L. et al. Outsourced privacy-preserving C4.5 decision tree algorithm over horizontally and vertically partitioned dataset among multiple parties. Cluster Comput 22 (Suppl 1), 1581–1593 (2019). https://doi.org/10.1007/s10586-017-1019-9

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