Abstract
Nowadays, mainstream natural language pro-cessing (NLP) is empowered by pre-trained language models. In the biomedical domain, only models pre-trained with anonymized data have been published. This policy is acceptable, but there are two questions: Can the privacy policy of language models be different from that of data? What happens if private language models are accidentally made public? We empirically evaluated the privacy risk of language models, using several BERT models pre-trained with MIMIC-III corpus in different data anonymity and corpus sizes. We simulated model inversion attacks to obtain the clinical information of target individuals, whose full names are already known to attackers. The BERT models were probably low-risk because the Top-100 accuracy of each attack was far below expected by chance. Moreover, most privacy leakage situations have several common primary factors; therefore, we formalized various privacy leakage scenarios under a universal novel framework named Knowledge, Anonymization, Resource, and Target (KART) framework. The KART framework helps parameterize complex privacy leakage scenarios and simplifies the comprehensive evaluation. Since the concept of the KART framework is domain agnostic, it can contribute to the establishment of privacy guidelines of language models beyond the biomedical domain.
Abstract (translated)
URL
https://arxiv.org/abs/2101.00036
