Analyzing leakage of personally identifiable information in language models
Language Models (LMs) have been shown to leak information about training data through
sentence-level membership inference and reconstruction attacks. Understanding the risk of …
sentence-level membership inference and reconstruction attacks. Understanding the risk of …
Are large pre-trained language models leaking your personal information?
Are Large Pre-Trained Language Models Leaking Your Personal Information? In this paper,
we analyze whether Pre-Trained Language Models (PLMs) are prone to leaking personal …
we analyze whether Pre-Trained Language Models (PLMs) are prone to leaking personal …
Quantifying privacy risks of masked language models using membership inference attacks
The wide adoption and application of Masked language models~(MLMs) on sensitive data
(from legal to medical) necessitates a thorough quantitative investigation into their privacy …
(from legal to medical) necessitates a thorough quantitative investigation into their privacy …
Downstream task performance of BERT models pre-trained using automatically de-identified clinical data
Automatic de-identification is a cost-effective and straightforward way of removing large
amounts of personally identifiable information from large and sensitive corpora. However …
amounts of personally identifiable information from large and sensitive corpora. However …
Do not give away my secrets: Uncovering the privacy issue of neural code completion tools
Neural Code Completion Tools (NCCTs) have reshaped the field of software development,
which accurately suggest contextually-relevant code snippets benefiting from language …
which accurately suggest contextually-relevant code snippets benefiting from language …
Disclosure control of machine learning models from trusted research environments (TRE): New challenges and opportunities
Introduction Artificial intelligence (AI) applications in healthcare and medicine have
increased in recent years. To enable access to personal data, Trusted Research …
increased in recent years. To enable access to personal data, Trusted Research …
Digger: Detecting copyright content mis-usage in large language model training
Pre-training, which utilizes extensive and varied datasets, is a critical factor in the success of
Large Language Models (LLMs) across numerous applications. However, the detailed …
Large Language Models (LLMs) across numerous applications. However, the detailed …
Membership inference attacks with token-level deduplication on korean language models
The confidentiality threat against training data has become a significant security problem in
neural language models. Recent studies have shown that memorized training data can be …
neural language models. Recent studies have shown that memorized training data can be …
End-to-end pseudonymization of fine-tuned clinical BERT models: Privacy preservation with maintained data utility
Many state-of-the-art results in natural language processing (NLP) rely on large pre-trained
language models (PLMs). These models consist of large amounts of parameters that are …
language models (PLMs). These models consist of large amounts of parameters that are …
Using membership inference attacks to evaluate privacy-preserving language modeling fails for pseudonymizing data
T Vakili, H Dalianis - Proceedings of the 24th Nordic Conference …, 2023 - aclanthology.org
Large pre-trained language models dominate the current state-of-the-art for many natural
language processing applications, including the field of clinical NLP. Several studies have …
language processing applications, including the field of clinical NLP. Several studies have …