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This paper proposes the hardware design and implementation of a neural network classifier resistant to power-based side-channel attacks using gate-level Boolean masking.

This paper demonstrates successful Differential Power Analysis (DPA) attacks on an FPGA-based binarized neural network classifier to extract the weights and biases. It also proposes effective masking and hiding-based countermeasures to thwart such attacks.