  Membership Inference Attacks (MIA) aim to infer whether a target data record
has been utilized for model training or not. Prior attempts have quantified the
privacy risks of language models (LMs) via MIAs, but there is still no
consensus on whether existing MIA algorithms can cause remarkable privacy
leakage on practical Large Language Models (LLMs). Existing MIAs designed for
LMs can be classified into two categories: reference-free and reference-based
attacks. They are both based on the hypothesis that training records
consistently strike a higher probability of being sampled. Nevertheless, this
hypothesis heavily relies on the overfitting of target models, which will be
mitigated by multiple regularization methods and the generalization of LLMs.
The reference-based attack seems to achieve promising effectiveness in LLMs,
which measures a more reliable membership signal by comparing the probability
discrepancy between the target model and the reference model. However, the
performance of reference-based attack is highly dependent on a reference
dataset that closely resembles the training dataset, which is usually
inaccessible in the practical scenario. Overall, existing MIAs are unable to
effectively unveil privacy leakage over practical fine-tuned LLMs that are
overfitting-free and private. We propose a Membership Inference Attack based on
Self-calibrated Probabilistic Variation (SPV-MIA). Specifically, since
memorization in LLMs is inevitable during the training process and occurs
before overfitting, we introduce a more reliable membership signal,
probabilistic variation, which is based on memorization rather than
overfitting. Furthermore, we introduce a self-prompt approach, which constructs
the dataset to fine-tune the reference model by prompting the target LLM
itself. In this manner, the adversary can collect a dataset with a similar
distribution from public APIs.
