Toward Private, Trusted, and Fine-Grained Inference Cloud Outsourcing with on-Device Obfuscation and Verification

More and more AI-enabled applications are deployed on numerous devices, which rely on cloud platforms to provide complicated AI models and execution. The untrusted cloud environment raises serious security concerns about user data privacy and remote computation integrity. However, existing approaches rely on expensive cryptographic or hardware methods which are rarely adopted by the cloud. This paper proposes a fine-grained outsourcing strategy that only outsources composite reversible computation inside inference tasks to the cloud. Composite reversible computation allows us to convert the results calculated using obfuscated data back to actual results. The cloud is not required to be trusted, and we employ obfuscation and verification at the device to guarantee secure and accurate inference execution. Thanks to the reversibility of the computation, processing on the obfuscated data does not lose model accuracy and the reversed results allow the device to verify the computation quality. We conduct a feasibility analysis and the preliminary result shows the on-device obfuscation and verification still have performance benefits in the case of fine-grained outsourced computation.