Uncovering Memorization in Timeseries Imputation models: LBRM Membership Inference and its link to attribute Leakage

Researchers have identified critical privacy vulnerabilities in deep learning models used for time series imputation, demonstrating that these models can leak sensitive training data through membership and attribute inference attacks. The study introduces a two-stage attack framework that successfully retrieves significant portions of training data even from models designed to be robust against overfitting-based attacks.