Bayesian Inference with Nonlinear Generative Models: Comments on Secure Learning

• URL: http://arxiv.org/abs/2201.09986v1
• Date: Wed, 19 Jan 2022 08:29:53 GMT
• Title: Bayesian Inference with Nonlinear Generative Models: Comments on Secure Learning
• Authors: Ali Bereyhi and Bruno Loureiro and Florent Krzakala and Ralf R. M\"uller and Hermann Schulz-Baldes
• Abstract summary: This work aims to bring attention to nonlinear generative models and their secrecy potential. We invoke the replica method to derive normalized cross entropy in an inverse probability problem. We propose a new secure coding scheme which achieves the secrecy capacity of the wiretap channel.
• Score: 29.818395770651865
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unlike the classical linear model, nonlinear generative models have been addressed sparsely in the literature. This work aims to bring attention to these models and their secrecy potential. To this end, we invoke the replica method to derive the asymptotic normalized cross entropy in an inverse probability problem whose generative model is described by a Gaussian random field with a generic covariance function. Our derivations further demonstrate the asymptotic statistical decoupling of Bayesian inference algorithms and specify the decoupled setting for a given nonlinear model. The replica solution depicts that strictly nonlinear models establish an all-or-nothing phase transition: There exists a critical load at which the optimal Bayesian inference changes from being perfect to an uncorrelated learning. This finding leads to design of a new secure coding scheme which achieves the secrecy capacity of the wiretap channel. The proposed coding has a significantly smaller codebook size compared to the random coding scheme of Wyner. This interesting result implies that strictly nonlinear generative models are perfectly secured without any secure coding. We justify this latter statement through the analysis of an illustrative model for perfectly secure and reliable inference.

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