TPRPF: a preserving framework of privacy relations based on adversarial training for texts in big data
Author:
Publisher
Springer Science and Business Media LLC
Subject
General Computer Science,Theoretical Computer Science
Link
https://link.springer.com/content/pdf/10.1007/s11704-022-1653-0.pdf
Reference9 articles.
1. 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
2. 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
3. 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
4. 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
5. 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
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