@inproceedings{yue-etal-2023-synthetic,
title = "Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipe",
author = "Yue, Xiang and
Inan, Huseyin and
Li, Xuechen and
Kumar, Girish and
McAnallen, Julia and
Shajari, Hoda and
Sun, Huan and
Levitan, David and
Sim, Robert",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.74",
doi = "10.18653/v1/2023.acl-long.74",
pages = "1321--1342",
abstract = "Privacy concerns have attracted increasing attention in data-driven products due to the tendency of machine learning models to memorize sensitive training data. Generating synthetic versions of such data with a formal privacy guarantee, such as differential privacy (DP), provides a promising path to mitigating these privacy concerns, but previous approaches in this direction have typically failed to produce synthetic data of high quality. In this work, we show that a simple and practical recipe in the text domain is effective: simply fine-tuning a pretrained generative language model with DP enables the model to generate useful synthetic text with strong privacy protection. Through extensive empirical analyses on both benchmark and private customer data, we demonstrate that our method produces synthetic text that is competitive in terms of utility with its non-private counterpart, meanwhile providing strong protection against potential privacy leakages.",
}
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<abstract>Privacy concerns have attracted increasing attention in data-driven products due to the tendency of machine learning models to memorize sensitive training data. Generating synthetic versions of such data with a formal privacy guarantee, such as differential privacy (DP), provides a promising path to mitigating these privacy concerns, but previous approaches in this direction have typically failed to produce synthetic data of high quality. In this work, we show that a simple and practical recipe in the text domain is effective: simply fine-tuning a pretrained generative language model with DP enables the model to generate useful synthetic text with strong privacy protection. Through extensive empirical analyses on both benchmark and private customer data, we demonstrate that our method produces synthetic text that is competitive in terms of utility with its non-private counterpart, meanwhile providing strong protection against potential privacy leakages.</abstract>
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%0 Conference Proceedings
%T Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipe
%A Yue, Xiang
%A Inan, Huseyin
%A Li, Xuechen
%A Kumar, Girish
%A McAnallen, Julia
%A Shajari, Hoda
%A Sun, Huan
%A Levitan, David
%A Sim, Robert
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F yue-etal-2023-synthetic
%X Privacy concerns have attracted increasing attention in data-driven products due to the tendency of machine learning models to memorize sensitive training data. Generating synthetic versions of such data with a formal privacy guarantee, such as differential privacy (DP), provides a promising path to mitigating these privacy concerns, but previous approaches in this direction have typically failed to produce synthetic data of high quality. In this work, we show that a simple and practical recipe in the text domain is effective: simply fine-tuning a pretrained generative language model with DP enables the model to generate useful synthetic text with strong privacy protection. Through extensive empirical analyses on both benchmark and private customer data, we demonstrate that our method produces synthetic text that is competitive in terms of utility with its non-private counterpart, meanwhile providing strong protection against potential privacy leakages.
%R 10.18653/v1/2023.acl-long.74
%U https://aclanthology.org/2023.acl-long.74
%U https://doi.org/10.18653/v1/2023.acl-long.74
%P 1321-1342
Markdown (Informal)
[Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipe](https://aclanthology.org/2023.acl-long.74) (Yue et al., ACL 2023)
ACL
- Xiang Yue, Huseyin Inan, Xuechen Li, Girish Kumar, Julia McAnallen, Hoda Shajari, Huan Sun, David Levitan, and Robert Sim. 2023. Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipe. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1321–1342, Toronto, Canada. Association for Computational Linguistics.