Are Large Pre-Trained Language Models Leaking Your Personal Information?
May 25, 2022 ยท Declared Dead ยท ๐ Conference on Empirical Methods in Natural Language Processing
"No code URL or promise found in abstract"
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Authors
Jie Huang, Hanyin Shao, Kevin Chen-Chuan Chang
arXiv ID
2205.12628
Category
cs.CL: Computation & Language
Cross-listed
cs.AI,
cs.CR
Citations
262
Venue
Conference on Empirical Methods in Natural Language Processing
Last Checked
3 months ago
Abstract
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 information. Specifically, we query PLMs for email addresses with contexts of the email address or prompts containing the owner's name. We find that PLMs do leak personal information due to memorization. However, since the models are weak at association, the risk of specific personal information being extracted by attackers is low. We hope this work could help the community to better understand the privacy risk of PLMs and bring new insights to make PLMs safe.
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