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Artificial neural network (ANN) is a supervised learning algorithm, whereparameters are learned by several back-and-forth iterations of passing theinputs through the network, comparing the output with the expected labels, andcorrecting the parameters. Inspired by a recent work of Boer and Kramer (2020),we investigate a different problem: Suppose an observer can view how the ANNparameters evolve over many iterations, but the dataset is oblivious to him.For instance, this can be an adversary eavesdropping on a multi-partycomputation of an ANN parameters (where intermediate parameters are leaked).Can he form a system of equations, and solve it to recover the dataset?

Neural Networks, Inside Out: Solving for Inputs Given Parameters (A Preliminary Investigation)