  As the deployment of pre-trained language models (PLMs) expands, pressing
security concerns have arisen regarding the potential for malicious extraction
of training data, posing a threat to data privacy. This study is the first to
provide a comprehensive survey of training data extraction from PLMs. Our
review covers more than 100 key papers in fields such as natural language
processing and security. First, preliminary knowledge is recapped and a
taxonomy of various definitions of memorization is presented. The approaches
for attack and defense are then systemized. Furthermore, the empirical findings
of several quantitative studies are highlighted. Finally, future research
directions based on this review are suggested.
