[Resource Topic] 2023/647: Efficient FHE-based Privacy-Enhanced Neural Network for AI-as-a-Service

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Efficient FHE-based Privacy-Enhanced Neural Network for AI-as-a-Service

Authors: Kwok-Yan Lam, Xianhui Lu, Linru Zhang, Xiangning Wang, Huaxiong Wang, Si Qi Goh


AI-as-a-Service has emerged as an important trend for supporting
the growth of the digital economy. Digital service providers make
use of their vast amount of user data to train AI models (such as
image recognitions, financial modelling and pandemic modelling
etc.) and offer them as a service on the cloud. While there are convincing advantages for using such third-party models, the fact that
users need to upload their data to the cloud is bound to raise serious
privacy concerns, especially in the face of increasingly stringent
privacy regulations and legislations.
To promote the adoption of AI-as-a-Service while addressing
the privacy issues, we propose a practical approach for constructing privacy-enhanced neural networks by designing an efficient
implementation of fully homomorphic encryption. With this approach, an existing neural network can be converted to process
FHE-encrypted data and produce encrypted output which are only
accessible by the model users, and more importantly, within an operationally acceptable time (e.g. within 1 second for facial recognition
in typical border control systems). Experimental results show that
in many practical tasks such as facial recognition, text classification
and so on, we obtained the state-of-the-art inference accuracy in
less than one second on a 16 cores CPU.

ePrint: https://eprint.iacr.org/2023/647

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