Xu, Qiongkai;
He, Xuanli;
(2023)
Security Challenges in Natural Language Processing Models.
In: Zhang, Qi and Sajjad, Hassan, (eds.)
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts.
(pp. pp. 7-12).
Association for Computational Linguistics: Singapore.
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Abstract
Large-scale natural language processing models have been developed and integrated into numerous applications, given the advantage of their remarkable performance. Nonetheless, the security concerns associated with these models prevent the widespread adoption of these black-box machine learning models. In this tutorial, we will dive into three emerging security issues in NLP research, i.e., backdoor attacks, private data leakage, and imitation attacks. These threats will be introduced in accordance with their threatening usage scenarios, attack methodologies, and defense technologies.
| Type: | Proceedings paper |
|---|---|
| Title: | Security Challenges in Natural Language Processing Models |
| Event: | 2023 Conference on Empirical Methods in Natural Language Processing |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.18653/v1/2023.emnlp-tutorial.2 |
| Publisher version: | https://doi.org/10.18653/v1/2023.emnlp-tutorial.2 |
| Language: | English |
| Additional information: | © The Author(s), 2023. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. https://creativecommons.org/licenses/by/4.0/ |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10188440 |
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