Information Leakage in Embedding Models

Understand

Embeddings are functions that map raw input data to low-dimensional vector representations, while preserving important semantic information about the inputs.

• Pre-training embeddings on a large amount of unlabeled data and fine-tuning them for downstream tasks is now a de facto standard in achieving state of the art learning in many domains.
• We demonstrate that embeddings, in addition to encoding generic semantics, often also present a vector that leaks sensitive information about the input data.
• We develop three classes of attacks to systematically study information that might be leaked by embeddings.