Privacy-Preserving ID3 Data Mining over Encrypted Data in Outsourced Environments with Multiple Keys

摘要

The emergence and development of the Internet resulted in the generation of huge amounts of data, which are often distributed among different sites. Many organizations and companies attempted to mine the data with cloud computing. However, given the rise of various privacy issues, sensitive data (e.g., medical records) need to be encrypted before outsourcing to the cloud. To process data mining, such as the ID3 algorithm, over encrypted data without the cloud decrypting the data is a very challenging task. In this paper, we consider a scenario where multiple parties with weak computational power need to run an ID3 algorithm on their databases jointly and outsource most of the computation of the protocol and databases to the cloud. In this scenario, each party can have the correct result calculated by the data from all of the parties with most of the computation outsourced to the cloud. For privacy, the data owned by each party should be kept confidential from both the other parties and the cloud. To ensure data privacy, we use an efficient homomorphic encryption scheme with multiple keys and design an Outsourced Secure Shared xlnx Protocol (OSSxlnx). We then propose a cloud-aided ID3 solution that is used to build an outsourced privacy-preserving ID3 data mining solution.