Variational Leakage: The Role of Information Complexity in Privacy Leakage

TL;DR

This paper investigates how information complexity influences privacy leakage in supervised representation learning, using neural networks to analyze various factors affecting leakage on image datasets.

Contribution

It introduces a variational framework to quantify privacy leakage and examines the impact of multiple parameters on information leakage in neural network models.

Findings

Higher information complexity regularization reduces leakage.

Latent space dimension affects the amount of leakage.

Correlation between utility and sensitive attributes influences leakage.

Abstract

We study the role of information complexity in privacy leakage about an attribute of an adversary's interest, which is not known a priori to the system designer. Considering the supervised representation learning setup and using neural networks to parameterize the variational bounds of information quantities, we study the impact of the following factors on the amount of information leakage: information complexity regularizer weight, latent space dimension, the cardinalities of the known utility and unknown sensitive attribute sets, the correlation between utility and sensitive attributes, and a potential bias in a sensitive attribute of adversary's interest. We conduct extensive experiments on Colored-MNIST and CelebA datasets to evaluate the effect of information complexity on the amount of intrinsic leakage.