Privacy-enhanced and non-interactive linear regression with dropout-resilience

Abstract

Linear regression, the most basic and widely used machine learning algorithm, has played an essential role in many areas such as healthcare, economics, and weather prediction. However, in practice, regression training requires a large dataset, usually stored in a distributed form from multiple data owners. Moreover, it is difficult to federate data among users to train a global model because of privacy issues and communication limitations. To address these challenges, we propose a non-interactive privacy-enhanced training scheme for linear regression based on functional encryption - FELR. In particular, based on the secure aggregation scheme and Paillier inner-product functional encryption, two cloud servers can train the model on the ciphertext. The solution does not require any interaction between the cloud servers and data owners. Throughout the training period, the cloud servers do not know any local private data. A detailed security analysis demonstrates that FELR can provide high security for data owners. The experimental conclusions also demonstrate that the performance of the training scheme on the data owners' side is approximately 25% higher than that of the existing solution, which significantly decreases the computational costs.