Heterogeneous Distributed Linear Regression Privacy-Preserving Modeling

Privacy-preserving is one of the most important and challenging issues in data mining field. It can help mining tools mine rules and patterns accurately while preserving the original private information of database. Statistical regression is a common tool in data mining field, but little work has been conducted to investigate how statistical analysis could be performed when data set is distributed among a number of data owners. Due to confidentiality or other proprietary reasons, data owners are reluctant to share data with others, while they wish to perform statistical analysis cooperatively. We address the important tradeoff between privacy and global statistical analysis. In this paper, the authors propose a homomorphous public key protocol based on ring homomorphism and discrete logarithm problem, and then constructe a privacy-preserving regression model, which can obtain accurate statistical results by using the homomorphous character of homomorphous public key protocol. Theoretical analysis and experiment results prove that the protocol and model are secure and effective.