Neural Networks, Inside Out: Solving for Inputs Given Parameters (A Preliminary Investigation)
Cryptography and Security
2021-10-13 v2 Machine Learning
Numerical Analysis
Numerical Analysis
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?
Cite
@article{arxiv.2110.03649,
title = {Neural Networks, Inside Out: Solving for Inputs Given Parameters (A Preliminary Investigation)},
author = {Mohammad Sadeq Dousti},
journal= {arXiv preprint arXiv:2110.03649},
year = {2021}
}