This is a preview of subscription content, log in via an institution to check access.
References
Li Y, Baldwin T, Cohn T. Towards robust and privacy-preserving text representation. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. 2018, 25–30
Coavoux M, Narayan S, Cohen S B. Privacy-preserving neural representations of text. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2018, 1–10
Lang J, Li L, Chen W, Zeng D. Privacy protection in transformer-based neural network. In: Proceedings of 2019 IEEE International Conference on Intelligence and Security Informatics (ISI). 2019, 182–184
Friedrich M, Köhn A, Wiedemann G, Biemann C. Adversarial learning of privacy-preserving text representations for de-identification of medical records. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019, 5829–5839
Jaiswal M, Provost E M. Privacy enhanced multimodal neural representations for emotion recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence. 2020, 7985–7993
Deng J, Guo J, Xue N, Zafeiriou S. Arcface: additive angular margin loss for deep face recognition. In: Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019, 4685–4694
Li A, Guo J, Yang H, Chen Y. Deepobfuscator: adversarial training framework for privacy-preserving image classification. 2019, arXiv preprint arXiv: 1909.04126
Han X, Zhu H, Yu P, Wang Z, Yao Y, Liu Z, Sun M. FewRel: a large-scale supervised few-shot relation classification dataset with state-of-the-art evaluation. In: Proceedings of 2018 Conference on Empirical Methods in Natural Language Processing. 2018, 4803–4809
Loria S. Textblob Documentation. See Buildmedia.readthedocs.org/media/pdf/textblob/latest/textblob.pdf website, 2018
Acknowledgements
This research was supported by the Guangdong Province Key Research and Development Plan (2019B010136003), the National Key Research and Development Plan (2018YFB1800702 and PCL2021A02), the National Natural Science Foundation of China (Grant Nos. 62002077, U20B2046, U1636215, 61871140, and U1803263), the Guangdong Higher Education Innovation Group 2020KCXTD007 and Guangzhou Higher Education Innovation Group 202032854, the Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme (2019), and the China Postdoctoral Science Foundation (2020M682657).
Author information
Authors and Affiliations
Corresponding authors
Electronic Supplementary Material
Rights and permissions
About this article
Cite this article
Chai, Y., Sun, Z., Qiu, J. et al. TPRPF: a preserving framework of privacy relations based on adversarial training for texts in big data. Front. Comput. Sci. 16, 164618 (2022). https://doi.org/10.1007/s11704-022-1653-0
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11704-022-1653-0