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Team streamlines neural networks to be more adept at computing on encrypted data


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BROOKLYN, New York, Wednesday, July 21, 2021 - This week, at the 38th International Conference on Machine Learning (ICML 21), researchers at the NYU Center for Cyber Security at the NYU Tandon School of Engineering are revealing new insights into the basic functions that drive the ability of neural networks to make inferences on encrypted data.
In the paper, DeepReDuce: ReLU Reduction for Fast Private Inference, the team focuses on linear and non-linear operators, key features of neural network frameworks that, depending on the operation, introduce a heavy toll in time and computational resources. When neural networks compute on encrypted data, many of these costs are incurred by rectified linear activation function (ReLU), a non-linear operation. ....

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