Outsourcing Privacy Preserving ID3 Decision Tree Algorithm over Encrypted Data-sets for Two-Parties

Abstract

ID3 decision tree data mining is a popular and widely studied data analysis technique for a range of applications. In this paper, we focus on the privacy-preserving ID3 decision tree algorithm on horizontally partitioned datasets. In such a scenario, data owners wish to learn the decision tree result from a collective data set but disclose minimal information about their own sensitive data. In this paper, we consider a scenario in which multiple parties with weak computational power need to run an ID3 algorithm on their databases jointly while simultaneously outsourcing most of the computation of the protocol and databases to the cloud. In such a scenario, each party can have the correct result calculated on the data from all the parties with most of the computation outsourced to the cloud. Concerning privacy, the data owned by each party should be kept confidential from both the other parties and the cloud. To ensure data privacy, we modify the Secure Equivalent Testing Protocol (SET) and design the Outsourced Secure Shared xlnx Protocol (OSSx ln x) and other sub-protocols. We then propose a cloud-aided ID3 solution based on these protocols, which is used to build an outsourced privacy-preserving ID3 data mining solution.