@inproceedings{klymenko-etal-2022-differential,
title = "Differential Privacy in Natural Language Processing The Story So Far",
author = "Klymenko, Oleksandra and
Meisenbacher, Stephen and
Matthes, Florian",
editor = "Feyisetan, Oluwaseyi and
Ghanavati, Sepideh and
Thaine, Patricia and
Habernal, Ivan and
Mireshghallah, Fatemehsadat",
booktitle = "Proceedings of the Fourth Workshop on Privacy in Natural Language Processing",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.privatenlp-1.1",
doi = "10.18653/v1/2022.privatenlp-1.1",
pages = "1--11",
abstract = "As the tide of Big Data continues to influence the landscape of Natural Language Processing (NLP), the utilization of modern NLP methods has grounded itself in this data, in order to tackle a variety of text-based tasks. These methods without a doubt can include private or otherwise personally identifiable information. As such, the question of privacy in NLP has gained fervor in recent years, coinciding with the development of new Privacy- Enhancing Technologies (PETs). Among these PETs, Differential Privacy boasts several desirable qualities in the conversation surrounding data privacy. Naturally, the question becomes whether Differential Privacy is applicable in the largely unstructured realm of NLP. This topic has sparked novel research, which is unified in one basic goal how can one adapt Differential Privacy to NLP methods? This paper aims to summarize the vulnerabilities addressed by Differential Privacy, the current thinking, and above all, the crucial next steps that must be considered.",
}
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%0 Conference Proceedings
%T Differential Privacy in Natural Language Processing The Story So Far
%A Klymenko, Oleksandra
%A Meisenbacher, Stephen
%A Matthes, Florian
%Y Feyisetan, Oluwaseyi
%Y Ghanavati, Sepideh
%Y Thaine, Patricia
%Y Habernal, Ivan
%Y Mireshghallah, Fatemehsadat
%S Proceedings of the Fourth Workshop on Privacy in Natural Language Processing
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F klymenko-etal-2022-differential
%X As the tide of Big Data continues to influence the landscape of Natural Language Processing (NLP), the utilization of modern NLP methods has grounded itself in this data, in order to tackle a variety of text-based tasks. These methods without a doubt can include private or otherwise personally identifiable information. As such, the question of privacy in NLP has gained fervor in recent years, coinciding with the development of new Privacy- Enhancing Technologies (PETs). Among these PETs, Differential Privacy boasts several desirable qualities in the conversation surrounding data privacy. Naturally, the question becomes whether Differential Privacy is applicable in the largely unstructured realm of NLP. This topic has sparked novel research, which is unified in one basic goal how can one adapt Differential Privacy to NLP methods? This paper aims to summarize the vulnerabilities addressed by Differential Privacy, the current thinking, and above all, the crucial next steps that must be considered.
%R 10.18653/v1/2022.privatenlp-1.1
%U https://aclanthology.org/2022.privatenlp-1.1
%U https://doi.org/10.18653/v1/2022.privatenlp-1.1
%P 1-11
Markdown (Informal)
[Differential Privacy in Natural Language Processing The Story So Far](https://aclanthology.org/2022.privatenlp-1.1) (Klymenko et al., PrivateNLP 2022)
ACL