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In the text processing context, most ML models are built on word embeddings.These embeddings are themselves trained on some datasets, potentiallycontaining sensitive data. In some cases this training is done independently,in other cases, it occurs as part of training a larger, task-specific model. Ineither case, it is of interest to consider membership inference attacks basedon the embedding layer as a way of understanding sensitive information leakage.But, somewhat surprisingly, membership inference attacks on word embeddings andtheir effect in other natural language processing (NLP) tasks that use theseembeddings, have remained relatively unexplored.  In this work, we show that word embeddings are vulnerable to black-boxmembership inference attacks under realistic assumptions. Furthermore, we showthat this leakage persists through two other major NLP applications:classification and text-generation, even when the embedding layer is notexposed to the attacker. We show that our MI attack achieves high attackaccuracy against a classifier model and an LSTM-based language model. Indeed,our attack is a cheaper membership inference attack on text-generative models,which does not require the knowledge of the target model or any expensivetraining of text-generative models as shadow models.

Membership Inference on Word Embedding and Beyond