How reparametrization trick broke differentially-private text representation learning

Ivan Habernal


Abstract
As privacy gains traction in the NLP community, researchers have started adopting various approaches to privacy-preserving methods. One of the favorite privacy frameworks, differential privacy (DP), is perhaps the most compelling thanks to its fundamental theoretical guarantees. Despite the apparent simplicity of the general concept of differential privacy, it seems non-trivial to get it right when applying it to NLP. In this short paper, we formally analyze several recent NLP papers proposing text representation learning using DPText (Beigi et al., 2019a,b; Alnasser et al., 2021; Beigi et al., 2021) and reveal their false claims of being differentially private. Furthermore, we also show a simple yet general empirical sanity check to determine whether a given implementation of a DP mechanism almost certainly violates the privacy loss guarantees. Our main goal is to raise awareness and help the community understand potential pitfalls of applying differential privacy to text representation learning.
Anthology ID:
2022.acl-short.87
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
771–777
Language:
URL:
https://aclanthology.org/2022.acl-short.87
DOI:
10.18653/v1/2022.acl-short.87
Bibkey:
Cite (ACL):
Ivan Habernal. 2022. How reparametrization trick broke differentially-private text representation learning. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 771–777, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
How reparametrization trick broke differentially-private text representation learning (Habernal, ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-short.87.pdf
Software:
 2022.acl-short.87.software.zip
Video:
 https://aclanthology.org/2022.acl-short.87.mp4
Code
 trusthlt/acl2022-reparametrization-trick-broke-differential-privacy