A new secure outsourcing ridge regression protocol with matrix encryption
Ou Ruan, Shanshan Qin · 2022
Ridge regression is an important algorithm in machine learning, which has a wide of practical applications such as recommendation systems. Therefore, ridge regression outsourcing scheme has been widely studied in recent years. However, it would be difficult to outsource ridge regression to an untrusted cloud server without giving away information. In this work, we propose a new secure and effective outsourcing ridge regression scheme. We use a series of disguise techniques with permutation matrices and unimodular matrices for encryption. Through theoretical and experimental analysis, our protocol has two advantages: 1) The protocol guarantees against malicious cloud server attack and the client can verify the results returned by the cloud server; 2) There is only one round of communication between the client and the cloud server. When the dimensionality increases, the computational cost of our protocol approaches the computational cost of the original problem.