Verifiable Privacy-Preserving Scheme Based on Vertical Federated Random Forest

Presently, under the condition of privacy preserving, vertical federated learning (VFL) has played an important role in training the machine learning (ML) models in the application scenarios, such as medical prediction, fraud detection, in which the data is distributed vertically. Random forest (RF) is one of the most widely used ML methods in VFL, which has the advantages of strong predictive performance, availability, and the ability to parallel tasks. However, current research on privacy preserving of vertical federated RF is limited, and none of them can achieve the application level of security, that is, in a system where users are dynamically changing, not only privacy can be preserved, but also data integrity can be verified. Therefore, we propose a verifiable privacy-preserving scheme (VPRF) based on vertical federated RF, in which the users are dynamic change. First, we design homomorphic comparison and voting statistics algorithms based on multikey homomorphic encryption for privacy preservation. Then, we propose a multiclient delegated computing verification algorithm to make up for the disadvantage that the above algorithms cannot verify data integrity. Finally, we used the data sets in UCI ML warehouse to evaluate the proposed scheme. The experiment results indicate that our scheme is more efficient than the existing ones that can achieve the same security level.