@inproceedings{gardiner-etal-2024-data,
title = "Data Anonymization for Privacy-Preserving Large Language Model Fine-Tuning on Call Transcripts",
author = "Gardiner, Shayna and
Habib, Tania and
Humphreys, Kevin and
Azizi, Masha and
Mailhot, Frederic and
Paling, Anne and
Thomas, Preston and
Zhang, Nathan",
editor = {Volodina, Elena and
Alfter, David and
Dobnik, Simon and
Lindstr{\"o}m Tiedemann, Therese and
Mu{\~n}oz S{\'a}nchez, Ricardo and
Szawerna, Maria Irena and
Vu, Xuan-Son},
booktitle = "Proceedings of the Workshop on Computational Approaches to Language Data Pseudonymization (CALD-pseudo 2024)",
month = mar,
year = "2024",
address = "St. Julian{'}s, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.caldpseudo-1.8",
pages = "64--75",
abstract = "Large language models in public-facing industrial applications must accurately process data for the domain in which they are deployed, but they must not leak sensitive or confidential information when used. We present a process for anonymizing training data, a framework for quantitatively and qualitatively assessing the effectiveness of this process, and an assessment of the effectiveness of models fine-tuned on anonymized data in comparison with commercially available LLM APIs.",
}
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%0 Conference Proceedings
%T Data Anonymization for Privacy-Preserving Large Language Model Fine-Tuning on Call Transcripts
%A Gardiner, Shayna
%A Habib, Tania
%A Humphreys, Kevin
%A Azizi, Masha
%A Mailhot, Frederic
%A Paling, Anne
%A Thomas, Preston
%A Zhang, Nathan
%Y Volodina, Elena
%Y Alfter, David
%Y Dobnik, Simon
%Y Lindström Tiedemann, Therese
%Y Muñoz Sánchez, Ricardo
%Y Szawerna, Maria Irena
%Y Vu, Xuan-Son
%S Proceedings of the Workshop on Computational Approaches to Language Data Pseudonymization (CALD-pseudo 2024)
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F gardiner-etal-2024-data
%X Large language models in public-facing industrial applications must accurately process data for the domain in which they are deployed, but they must not leak sensitive or confidential information when used. We present a process for anonymizing training data, a framework for quantitatively and qualitatively assessing the effectiveness of this process, and an assessment of the effectiveness of models fine-tuned on anonymized data in comparison with commercially available LLM APIs.
%U https://aclanthology.org/2024.caldpseudo-1.8
%P 64-75
Markdown (Informal)
[Data Anonymization for Privacy-Preserving Large Language Model Fine-Tuning on Call Transcripts](https://aclanthology.org/2024.caldpseudo-1.8) (Gardiner et al., CALD-pseudo-WS 2024)
ACL
- Shayna Gardiner, Tania Habib, Kevin Humphreys, Masha Azizi, Frederic Mailhot, Anne Paling, Preston Thomas, and Nathan Zhang. 2024. Data Anonymization for Privacy-Preserving Large Language Model Fine-Tuning on Call Transcripts. In Proceedings of the Workshop on Computational Approaches to Language Data Pseudonymization (CALD-pseudo 2024), pages 64–75, St. Julian’s, Malta. Association for Computational Linguistics.