  Transfer learning has become an increasingly popular technique in machine
learning as a way to leverage a pretrained model trained for one task to assist
with building a finetuned model for a related task. This paradigm has been
especially popular for privacy in machine learning, where the pretrained model
is considered public, and only the data for finetuning is considered sensitive.
However, there are reasons to believe that the data used for pretraining is
still sensitive, making it essential to understand how much information the
finetuned model leaks about the pretraining data. In this work we propose a new
membership-inference threat model where the adversary only has access to the
finetuned model and would like to infer the membership of the pretraining data.
To realize this threat model, we implement a novel metaclassifier-based attack,
TMI, that leverages the influence of memorized pretraining samples on
predictions in the downstream task. We evaluate TMI on both vision and natural
language tasks across multiple transfer learning settings, including finetuning
with differential privacy. Through our evaluation, we find that TMI can
successfully infer membership of pretraining examples using query access to the
finetuned model.

Machine Learning;Cryptography and Security