Blindfolded Evaluation of Random Forests with Multi-Key Homomorphic Encryption

Decision tree and its generalization of random forests are a simple yet powerful machine learning model for many classification and regression problems. Recent works propose how to privately evaluate a decision tree in a two-party setting where the feature vector of the client or the decision tree model (such as the threshold values of its nodes) is kept secret from another party. However, these works cannot be extended trivially to support the outsourcing setting where a third-party who should not have access to the model or the query. Furthermore, their use of an interactive comparison protocol does not support branching program, hence requires interactions with the client to determine the comparison result before resuming the evaluation task. In this paper, we propose the first secure protocol for collaborative evaluation of random forests contributed by multiple owners. They outsource evaluation tasks to a third-party evaluator. Upon receiving the client's encrypted inputs, the cloud evaluates obliviously on individually encrypted random forest models and calculates the aggregated result. The system is based on our new secure comparison protocol, secure counting protocol, and a multi-key somewhat homomorphic encryption on top of symmetric-key encryption. This allows us to reduce communication overheads while achieving round complexity lower than existing work.

Automated summary

The paper introduces a secure protocol for collaborative evaluation of random forests contributed by multiple owners, outsourced to a third-party evaluator. The cloud evaluates obliviously on individually encrypted random forest models and calculates the aggregated result, based on a new secure comparison protocol, secure counting protocol, and multi-key somewhat homomorphic encryption to reduce communication overheads and round complexity.