Cryptology ePrint Archive: Report 2018/1056

The AlexNet Moment for Homomorphic Encryption: HCNN, the First Homomorphic CNN on Encrypted Data with GPUs

Ahmad Al Badawi and Jin Chao and Jie Lin and Chan Fook Mun and Sim Jun Jie and Benjamin Hong Meng Tan and Xiao Nan and Khin Mi Mi Aung and Vijay Ramaseshan Chandrasekhar

Abstract: Fully homomorphic encryption, with its widely-known feature of computing on encrypted data, empowers a wide range of privacy-concerned cloud applications including deep learning as a service. This comes at a high cost since FHE includes highly-intensive computation that requires enormous computing power. Although the literature includes a number of proposals to run CNNs on encrypted data, the performance is still far from satisfactory. In this paper, we push the level up and show how to accelerate the performance of running CNNs on encrypted data using GPUs. We evaluated a CNN to classify homomorphically the MNIST dataset into 10 classes. We used a number of techniques such as low-precision training, unified training and testing network, optimized FHE parameters and a very efficient GPU implementation to achieve high performance. Our solution achieved high security level ($> 128$ bit) and high accuracy (99\%). In terms of performance, our best results show that we could classify the entire testing dataset in 14.105 seconds, with per-image amortized time (1.411 milliseconds) 40.41$\times$ faster than prior art.

Category / Keywords: implementation / Fully Homomorphic Encryption, Deep Learning, Encrypted CNN, Privacy-preserving Computing, GPU Acceleration

Date: received 31 Oct 2018

Contact author: a0135956 at u nus edu

Available format(s): PDF | BibTeX Citation

Version: 20181102:011842 (All versions of this report)

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