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When neural network model and data are outsourced to cloud server forinference, it is desired to preserve the confidentiality of model and data asthe involved parties (i.e., cloud server, model providing client and dataproviding client) may not trust mutually. Solutions were proposed based onmulti-party computation, trusted execution environment (TEE) and leveled orfully homomorphic encryption (LHE/FHE), but their limitations hamper practicalapplication. We propose a new framework based on synergistic integration of LHEand TEE, which enables collaboration among mutually-untrusted three parties,while minimizing the involvement of (relatively) resource-constrained TEE andallowing the full utilization of the untrusted but more resource-rich part ofserver. We also propose a generic and efficient LHE-based inference scheme asan important performance-determining component of the framework. Weimplemented/evaluated the proposed system on a moderate platform and show that,our proposed scheme is more applicable/scalable to various settings, and hasbetter performance, compared to the state-of-the-art LHE-based solutions.

Towards Practical Privacy-Preserving Solution for Outsourced Neural Network Inference