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Fast and Private Inference of Deep Neural Networks by Co-designing Activation Functions
April 17, 2024, 4:11 a.m. | Abdulrahman Diaa, Lucas Fenaux, Thomas Humphries, Marian Dietz, Faezeh Ebrahimianghazani, Bailey Kacsmar, Xinda Li, Nils Lukas, Rasoul Akhavan Mahdavi
cs.CR updates on arXiv.org arxiv.org
Abstract: Machine Learning as a Service (MLaaS) is an increasingly popular design where a company with abundant computing resources trains a deep neural network and offers query access for tasks like image classification. The challenge with this design is that MLaaS requires the client to reveal their potentially sensitive queries to the company hosting the model. Multi-party computation (MPC) protects the client's data by allowing encrypted inferences. However, current approaches suffer from prohibitively large inference times. …
access arxiv challenge classification client computing cs.cr cs.lg design fast functions image machine machine learning network networks neural network neural networks popular private query resources service trains
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