Abstract
As a booming research area in the past decade, deep learning technologies have been driven by big data collected and processed on an unprecedented scale. However, the sensitive information in the collected training data raises privacy concerns. Recent research indicated that deep learning models are vulnerable to various privacy attacks, including membership inference attacks, attribute inference attacks, and gradient inversion attacks. It is noteworthy that the performance of the attacks varies from model to model. In this paper, we conduct empirical analyses to answer a fundamental question: Does model architecture affect model privacy? We investigate several representative model architectures from CNNs to Transformers, and show that Transformers are generally more vulnerable to privacy attacks than CNNs. We further demonstrate that the micro design of activation layers, stem layers, and bias parameters, are the major reasons why CNNs are more resilient to privacy attacks than Transformers. We also find that the presence of attention modules is another reason why Transformers are more vulnerable to privacy attacks. We hope our discovery can shed some new light on how to defend against the investigated privacy attacks and help the community build privacy-friendly model architectures.
Abstract (translated)
URL
https://arxiv.org/abs/2210.11049
