Privacy-Preserving Natural Language Processing
Ivan Habernal, Fatemehsadat Mireshghallah, Patricia Thaine, Sepideh Ghanavati, Oluwaseyi Feyisetan
Abstract
This cutting-edge tutorial will help the NLP community to get familiar with current research in privacy-preserving methods. We will cover topics as diverse as membership inference, differential privacy, homomorphic encryption, or federated learning, all with typical applications to NLP. The goal is not only to draw the interest of the broader community, but also to present some typical use-cases and potential pitfalls in applying privacy-preserving methods to human language technologies.- Anthology ID:
- 2023.eacl-tutorials.6
- Volume:
- Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts
- Month:
- May
- Year:
- 2023
- Address:
- Dubrovnik, Croatia
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 27–30
- Language:
- URL:
- https://aclanthology.org/2023.eacl-tutorials.6
- DOI:
- Cite (ACL):
- Ivan Habernal, Fatemehsadat Mireshghallah, Patricia Thaine, Sepideh Ghanavati, and Oluwaseyi Feyisetan. 2023. Privacy-Preserving Natural Language Processing. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts, pages 27–30, Dubrovnik, Croatia. Association for Computational Linguistics.
- Cite (Informal):
- Privacy-Preserving Natural Language Processing (Habernal et al., EACL 2023)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/2023.eacl-tutorials.6.pdf