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arXiv · 2110.03649

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

Mohammad Sadeq Dousti
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Abstract

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

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Mohammad Sadeq Dousti. 2021-10-07. Neural Networks, Inside Out: Solving for Inputs Given Parameters (A Preliminary Investigation). https://arxiv.org/abs/2110.03649

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