Supervised Joint Nonlinear Transform Learning with Discriminative-Ambiguous Prior for Generic Privacy-Preserved Features

摘要

In this paper, we explicitly model a discriminative-ambiguous setup by two jointly learned parametric nonlinear transforms. The idea is to use one nonlinear transform for ambiguization and the other one for discrimination, and also to address a privacy-utility setup that is conditioned on ambiguization and discrimination priors, respectively, together with minimum information loss prior. The generic coupled representation is composed by linear combination using the two nonlinear transforms. The model parameters are learned by minimizing the empirical log likelihood of the model, where we propose an efficient solution using block coordinate descend alternating algorithm. The proposed model has low computational complexity and high recognition accuracy for the authorized parties while having low recognition accuracy for the unauthorized parties. We validate the potential of the proposed approach by numerical experiments.