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Found 4 papers, 0 papers with code
Copyright Traps for Large Language Models
We here propose to use copyright traps, the inclusion of fictitious entries in original content, to detect the use of copyrighted materials in LLMs with a focus on models where memorization does not naturally occur.
Did the Neurons Read your Book? Document-level Membership Inference for Large Language Models
First, we propose a procedure for the development and evaluation of document-level membership inference for LLMs by leveraging commonly used data sources for training and the model release date.
Synthetic is all you need: removing the auxiliary data assumption for membership inference attacks against synthetic data
While membership inference attacks (MIAs), based on shadow modeling, have become the standard to evaluate the privacy of synthetic data, they currently assume the attacker to have access to an auxiliary dataset sampled from a similar distribution as the training dataset.
Achilles' Heels: Vulnerable Record Identification in Synthetic Data Publishing
The choice of vulnerable records is as important as more accurate MIAs when evaluating the privacy of synthetic data releases, including from a legal perspective.
