Privacy Leakage in Text Classification A Data Extraction Approach

Adel Elmahdy, Huseyin A. Inan, Robert Sim


Abstract
Recent work has demonstrated the successful extraction of training data from generative language models. However, it is not evident whether such extraction is feasible in text classification models since the training objective is to predict the class label as opposed to next-word prediction. This poses an interesting challenge and raises an important question regarding the privacy of training data in text classification settings. Therefore, we study the potential privacy leakage in the text classification domain by investigating the problem of unintended memorization of training data that is not pertinent to the learning task. We propose an algorithm to extract missing tokens of a partial text by exploiting the likelihood of the class label provided by the model. We test the effectiveness of our algorithm by inserting canaries into the training set and attempting to extract tokens in these canaries post-training. In our experiments, we demonstrate that successful extraction is possible to some extent. This can also be used as an auditing strategy to assess any potential unauthorized use of personal data without consent.
Anthology ID:
2022.privatenlp-1.3
Volume:
Proceedings of the Fourth Workshop on Privacy in Natural Language Processing
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
PrivateNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
13–20
Language:
URL:
https://aclanthology.org/2022.privatenlp-1.3
DOI:
10.18653/v1/2022.privatenlp-1.3
Bibkey:
Cite (ACL):
Adel Elmahdy, Huseyin A. Inan, and Robert Sim. 2022. Privacy Leakage in Text Classification A Data Extraction Approach. In Proceedings of the Fourth Workshop on Privacy in Natural Language Processing, pages 13–20, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Privacy Leakage in Text Classification A Data Extraction Approach (Elmahdy et al., PrivateNLP 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.privatenlp-1.3.pdf