Title: Outsourcing privacy-preserving ID3 decision tree over horizontally partitioned data for multiple parties

Authors: Ye Li; Xuan Wang; Zoe L. Jiang; S.M. Yiu

Addresses: Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, 518055, China; Shenzhen Applied Technology Engineering Laboratory for Internet Multimedia Application, Shenzhen, 518055, China ' Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, 518055, China; Shenzhen Applied Technology Engineering Laboratory for Internet Multimedia Application, Shenzhen, 518055, China ' Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, 518055, China; Guangdong Provincial Key Laboratory of High Performance Computing, Guangzhou, 510275, China ' The University of Hong Kong, Hong Kong

Abstract: Today, many small and medium-sized companies want to share data for data mining; however, privacy and security concerns restrict such data sharing. Privacy-preserving data mining has emerged as a solution to this problem. Nevertheless, the traditional cryptographic solutions are too inefficient and infeasible to allow the large-scale analytics needed for big data. In this paper, we focus on the outsourcing of privacy-preserving ID3 decision trees over horizontally partitioned data for multiple parties. We outsource most of the protocol computation to the cloud and propose the OPPWAP to protect users' data privacy. By this method, each party can have the correct results calculated with data from other parties and the cloud, and each party's data are kept private from other parties and the cloud. Our findings indicate that an increase in the number of participating parties results in a slight computing cost increase on the user's side.

Keywords: cloud computing; privacy-preserving data mining; decision tree; horizontally partitioned data; multiple parties; privacy-preserving weighted average problem; PPWAP.

DOI: 10.1504/IJHPCN.2018.094370

International Journal of High Performance Computing and Networking, 2018 Vol.12 No.2, pp.207 - 215

Received: 11 May 2016
Accepted: 24 Aug 2016
Published online: 31 Aug 2018 *

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