6533b7d8fe1ef96bd126af41

RESEARCH PRODUCT

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

Mohammad Sadeq Dousti

subject

FOS: Computer and information sciencesComputer Science - Machine LearningComputingMethodologies_PATTERNRECOGNITIONComputer Science - Cryptography and SecurityComputer Science::Neural and Evolutionary ComputationFOS: MathematicsNumerical Analysis (math.NA)Mathematics - Numerical AnalysisCryptography and Security (cs.CR)Computer Science::DatabasesMachine Learning (cs.LG)Computer Science::Cryptography and Security

description

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?

yearjournalcountryeditionlanguage
2021-10-07
http://arxiv.org/abs/2110.03649