Optimizing Secure Decision Tree Inference Outsourcing

TL;DR

This paper presents a new system for secure outsourcing of decision tree inference that significantly reduces latency and resource usage for both cloud providers and clients, enhancing privacy-preserving machine learning services.

Contribution

The paper introduces novel schemes that shift most processing to the cloud and optimize encrypted inference, achieving substantial performance improvements over existing methods.

Findings

Up to 8x better end-to-end latency in WAN environments

Up to 19x savings in communication costs for the model provider

Up to 18x savings in computation for the model provider

Abstract

Outsourcing decision tree inference services to the cloud is highly beneficial, yet raises critical privacy concerns on the proprietary decision tree of the model provider and the private input data of the client. In this paper, we design, implement, and evaluate a new system that allows highly efficient outsourcing of decision tree inference. Our system significantly improves upon the state-of-the-art in the overall online end-to-end secure inference service latency at the cloud as well as the local-side performance of the model provider. We first presents a new scheme which securely shifts most of the processing of the model provider to the cloud, resulting in a substantial reduction on the model provider's performance complexities. We further devise a scheme which substantially optimizes the performance for encrypted decision tree inference at the cloud, particularly the communication round complexities. The synergy of these techniques allows our new system to achieve up to $8 \times$ better overall online end-to-end secure inference latency at the cloud side over realistic WAN environment, as well as bring the model provider up to $19 \times$ savings in communication and $18 \times$ savings in computation.