Outsourcing privacy-preserving ID3 decision tree over horizontal partitioned data for multiple parties
S.M. Yiu, Zoe L. Jiang, Xuan Wang, Ye Li · International Journal of High Performance Computing and Networking · 2016
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.