2022
DOI: 10.1145/3501809
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Toward Scalable and Privacy-preserving Deep Neural Network via Algorithmic-Cryptographic Co-design

Abstract: Deep Neural Networks (DNNs) have achieved remarkable progress in various real-world applications, especially when abundant training data are provided. However, data isolation has become a serious problem currently. Existing works build privacy preserving DNN models from either algorithmic perspective or cryptographic perspective. The former mainly splits the DNN computation graph between data holders or between data holders and server, which demonstrates good sca… Show more

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Cited by 2 publications
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