On the Privacy Risks of Deploying Recurrent Neural Networks in Machine Learning Models. (arXiv:2110.03054v3 [cs.CR] UPDATED)

We study the privacy implications of training recurrent neural networks
(RNNs) with sensitive training datasets. Considering membership inference
attacks (MIAs), which aim to infer whether or not specific data records have
been used in training a given machine learning model, we provide empirical
evidence that a neural network's architecture impacts its vulnerability to
MIAs. In particular, we demonstrate that RNNs are subject to a higher attack
accuracy than feed-forward neural network (FFNN) counterparts. Additionally, we
study the effectiveness of two …

machine machine learning machine learning models networks privacy