METHOD FOR PROTECTING A MACHINE LEARNING MODEL FROM A SIDE CHANNEL ATTACK

A method is provided for protecting a machine learning (ML) model from a side channel attack (SCA). The method is executed by a processor in a data processing system. The method includes generating a first random bit. A first weighted sum is computed for a first connection between a node of a first layer and a node of a second layer of the ML model. The first weighted sum for the first connection is equal to a multiplication of the weight of the first connection multiplied by an input to the selected node. In the multiplication, one of the weight or the input is negated conditioned on a value of the random bit. A first output including the computed first weighted sum is provided to one or more nodes of a second layer of the plurality of layers.