25675

AsymML: An Asymmetric Decomposition Framework for Privacy-Preserving DNN Training and Inference

Yue Niu, Ramy E. Ali, Salman Avestimehr
ECE Department, University of Southern California (USC)
arXiv:2110.01229 [cs.CR], (4 Oct 2021)

@misc{niu2021asymml,

   title={AsymML: An Asymmetric Decomposition Framework for Privacy-Preserving DNN Training and Inference},

   author={Yue Niu and Ramy E. Ali and Salman Avestimehr},

   year={2021},

   eprint={2110.01229},

   archivePrefix={arXiv},

   primaryClass={cs.CR}

}

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Leveraging parallel hardware (e.g. GPUs) to conduct deep neural network (DNN) training/inference, though significantly speeds up the computations, raises several data privacy concerns. Trusted execution environments (TEEs) have emerged as a promising solution to enable privacy-preserving inference and training. TEEs, however, have limited memory and computation resources which renders it not comparable to untrusted parallel hardware in performance. To mitigate the trade-off between privacy and computing performance, we propose an asymmetric model decomposition framework, AsymML, to (1) accelerate training/inference using parallel hardware; and (2) preserve privacy using TEEs. By exploiting the low-rank characteristics in data and intermediate features, AsymML asymmetrically splits a DNN model into trusted and untrusted parts: the trusted part features privacy-sensitive data but incurs small compute/memory costs; while the untrusted part is computationally-intensive but not privacy-sensitive. Computing performance and privacy are guaranteed by respectively delegating the trusted and untrusted part to TEEs and GPUs. Furthermore, we present a theoretical rank bound analysis showing that low-rank characteristics are still preserved in intermediate features, which guarantees efficiency of AsymML. Extensive evaluations on DNN models shows that AsymML delivers 11.2x speedup in inference, 7.6x in training compared to the TEE-only executions.
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