Privacy-preserving lightweight face recognition-Reference-Cited by-同舟云学术

Privacy-preserving lightweight face recognition

Published:2019-10 Issue: Volume:363 Page:212-222
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Li Yuancheng,Wang Yimeng^ORCID,Li Daoxing^ORCID

Funder

State Grid Corporation of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference40 articles.

1. Deep learning with differential privacy;Abadi,2016

2. Differentially private empirical risk minimization: efficient algorithms and tight error bounds;Bassily;Comput. Sci.,2014

3. Evaluating open-universe face identification on the web;Becker,2013

4. M. Bun, T. Steinke, Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds. eprint arXiv:1605.02065, 2016: p. arXiv:1605.02065.

5. Q. Cao, et al., VGGFace2: A dataset for recognising faces across pose and age. eprint arXiv:1710.08092, 2017: p. arXiv:1710.08092.

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