A Privacy-Preserving Deep Learning Approach for Face Recognition with Edge Computing


Yunlong Mao, Nanjing University; Shanhe Yi and Qun Li, College of William & Mary; Jinghao Feng, Fengyuan Xu, and Sheng Zhong, Nanjing University


Deep convolutional neural networks (DNNs) have brought significant performance improvements to face recognition. However the training can hardly be carried out on mobile devices because the training of these models requires much computational power. An individual user with the demand of deriving DNN models from her own datasets usually has to outsource the training procedure onto a cloud or edge server. However this outsourcing method violates privacy because it exposes the users' data to curious service providers. In this paper, we utilize the differentially private mechanism to enable the privacy-preserving edge based training of DNN face recognition models. During the training, DNN is split between the user device and the edge server in a way that both private data and model parameters are protected, with only a small cost of local computations. We show that our mechanism is capable of training models in different scenarios, e.g., from scratch, or through fine-tuning over existed models.

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@inproceedings {216777,
title = {A {Privacy-Preserving} Deep Learning Approach for Face Recognition with Edge Computing},
booktitle = {USENIX Workshop on Hot Topics in Edge Computing (HotEdge 18)},
year = {2018},
address = {Boston, MA},
url = {https://www.usenix.org/conference/hotedge18/presentation/mao},
publisher = {USENIX Association},
month = jul,