From Individual Computation to Allied Optimization: Remodeling Privacy-Preserving Neural Inference with Function Input Tuning-Reference-Cited by-同舟云学术

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From Individual Computation to Allied Optimization: Remodeling Privacy-Preserving Neural Inference with Function Input Tuning

Published:2024-05-19 Issue: Volume:2012 Page:4810-4827
ISSN:
Container-title:2024 IEEE Symposium on Security and Privacy (SP)
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Author:

Zhang Qiao¹,Xiang Tao¹,Xin Chunsheng²,Wu Hongyi³

Affiliation:

1. Chongqing University,College of Computer Science,Chongqing,China,400044

2. Old Dominion University,Department of Electrical and Computer Engineering,Norfolk,VA,USA,23529

3. The University of Arizona,Department of Electrical and Computer Engineering,Tucson,AZ,USA,85721

Funder

National Natural Science Foundation of China

China Postdoctoral Science Foundation

Fundamental Research Funds for the Central Universities

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10646615/10646598/10646638.pdf?arnumber=10646638

Reference48 articles.

1. Gradient-based learning applied to document recognition

2. Very deep convolutional networks for large-scale image recognition;Simonyan,2014

3. Deep Residual Learning for Image Recognition

4. Measurement Reliability and Validity

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