Christopher A. Choquette-Choo
3 papers in the PaperMetrix corpus
Papers by this author
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Preventing Verbatim Memorization in Language Models Gives a False Sense of Privacy
2022 · arXiv (Cornell University)
Studying data memorization in neural language models helps us understand the risks (e.g., to privacy or copyright) associated with models regurgitating training data and aids in the development of countermeasures. Many prior works -- and …
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User Inference Attacks on Large Language Models
2023 · arXiv (Cornell University)
Fine-tuning is a common and effective method for tailoring large language models (LLMs) to specialized tasks and applications. In this paper, we study the privacy implications of fine-tuning LLMs on user data. To this end, …
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Privacy Amplification for Matrix Mechanisms
2023 · arXiv (Cornell University)
Privacy amplification exploits randomness in data selection to provide tighter differential privacy (DP) guarantees. This analysis is key to DP-SGD's success in machine learning, but, is not readily applicable to the newer state-of-the-art algorithms. This …