On the Application of Binary Neural Networks in Oblivious Inference
This paper explores the application of Binary Neural Networks (BNN) in oblivious inference, a service provided by a server to mistrusting clients. Using this service, a client can obtain the inference result on her data by a trained model held by the server without disclosing the data or leaning the...
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Published in | 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) pp. 4625 - 4634 |
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Main Authors | , , , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
01.06.2021
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Subjects | |
Online Access | Get full text |
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Summary: | This paper explores the application of Binary Neural Networks (BNN) in oblivious inference, a service provided by a server to mistrusting clients. Using this service, a client can obtain the inference result on her data by a trained model held by the server without disclosing the data or leaning the model parameters. We make two contributions to this field. First, we devise light-weight cryptographic protocols designed specifically to exploit the unique characteristics of BNNs. Second, we present dynamic exploration of the runtime-accuracy tradeoff of BNNs in a single-shot training process. While previous works trained multiple BNNs with different computational complexities (which is cumbersome due to the slow convergence of BNNs), we train a single BNN that can perform inference under different computational budgets. Compared to Crypt-Flow2, the state-of-the-art in oblivious inference of non-binary DNNs, our approach reaches 2× faster inference at the same accuracy. Compared to XONN, the state-of-the-art in oblivious inference of binary networks, we achieve 2× to 11× faster inference while obtaining higher accuracy. |
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ISSN: | 2160-7516 |
DOI: | 10.1109/CVPRW53098.2021.00521 |