Researchers streamline neural networks to compute on encrypted data

July 22, 2021 // By Jean-Pierre Joosting
Researchers streamline neural networks to compute on encrypted data
Run time costs incurred by rectified linear activation functions when neural networks make inferences on encrypted data are reduced significantly with improved accuracy.

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 revealed 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.

Brandon Reagen, professor of computer science and engineering and electrical and computer engineering and a team of collaborators including Nandan Kumar Jha, a Ph.D. student, and Zahra Ghodsi, a former doctoral student under the guidance of Siddharth Garg, developed a framework called DeepReDuce. It offers a solution through rearrangement and reduction of ReLUs in neural networks.

Reagen explained that this shift requires a fundamental reassessment of where and how many components are distributed in neural networks systems.

"What we are trying to do is rethink how neural nets are designed in the first place," he explained.

"You can skip a lot of these time- and computationally-expensive ReLU operations and still get high performing networks at 2 to 4 times faster run time."

The team found that, compared to the state-of-the-art for private inference, DeepReDuce improved accuracy and reduced ReLU count by up to 3.5% and 3.5×, respectively.


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