TY - GEN
T1 - Differential Privacy in Natural Language Processing
T2 - 4th Workshop on Privacy in Natural Language Processing, PrivateNLP 2022
AU - Klymenko, Oleksandra
AU - Meisenbacher, Stephen
AU - Matthes, Florian
N1 - Publisher Copyright:
© 2022 Association for Computational Linguistics.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85139162829&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85139162829
T3 - PrivateNLP 2022 - 4th Workshop on Privacy in Natural Language Processing at the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Workshop
SP - 1
EP - 11
BT - PrivateNLP 2022 - 4th Workshop on Privacy in Natural Language Processing at the 2022 Conference of the North American Chapter of the Association for Computational Linguistics
A2 - Feyisetan, Oluwaseyi
A2 - Ghanavati, Sepideh
A2 - Thaine, Patricia
A2 - Habernal, Ivan
A2 - Mireshghallah, Fatemehsadat
PB - Association for Computational Linguistics (ACL)
Y2 - 15 July 2022
ER -