L. Hu, I. Habernal, L. Shen, and D. Wang, “Differentially Private Natural Language Models: Recent Advances and Future Directions,” in Findings of the Association for Computational Linguistics: EACL 2024, St. Julian’s, Malta, March 17-22, 2024, 2024, pp. 478–499.

T. Igamberdiev, D. N. L. Vu, F. Kuennecke, Z. Yu, J. Holmer, and I. Habernal, “DP-NMT: Scalable Differentially Private Machine Translation,” in Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations, 2024, pp. 94–105.

I. Habernal, F. Mireshghallah, P. Thaine, S. Ghanavati, and O. Feyisetan, “Privacy-Preserving Natural Language Processing,”10.18653/v1/2023.eacl-tutorials.6__2023, doi:10.18653/v1/2023.eacl-tutorials.6 .

C. Matzken, S. Eger, and I. Habernal, “Trade-Offs Between Fairness and Privacy in Language Modeling,”10.18653/v1/2023.findings-acl.434__2023, doi:10.18653/v1/2023.findings-acl.434 .

N. Mouhammad, J. Daxenberger, B. Schiller, and I. Habernal, “Crowdsourcing on Sensitive Data with Privacy-Preserving Text Rewriting,”10.18653/v1/2023.law-1.8__2023, doi:10.18653/v1/2023.law-1.8 .

Y. Yin and I. Habernal, “Privacy-Preserving Models for Legal Natural Language Processing,”10.18653/v1/2022.nllp-1.14__2023, doi:10.18653/v1/2022.nllp-1.14 .

R. Sawhney, A. Neerkaje, I. Habernal, and L. Flek, “How Much User Context Do We Need? Privacy by Design in Mental Health NLP Applications,”10.1609/icwsm.v17i1.22186__Proceedings of the International AAAI Conference on Web and Social Media, vol. 17, pp. 766–776, 2023, doi:10.1609/icwsm.v17i1.22186 .

M. Senge, T. Igamberdiev, and I. Habernal, “One size does not fit all: Investigating strategies for differentially-private learning across NLP tasks,”10.18653/v1/2022.emnlp-main.496__2023, doi:10.18653/v1/2022.emnlp-main.496 .

T. Igamberdiev and I. Habernal, “DP-BART for Privatized Text Rewriting under Local Differential Privacy,”10.18653/v1/2023.findings-acl.874__2023, doi:10.18653/v1/2023.findings-acl.874 .

L. Bongard, L. Held, and I. Habernal, “The Legal Argument Reasoning Task in Civil Procedure,”10.18653/v1/2022.nllp-1.17__2023, doi:10.18653/v1/2022.nllp-1.17 .

I. Habernal et al., “Mining legal arguments in court decisions,”10.1007/s10506-023-09361-y__Artificial Intelligence and Law, 2023, doi:10.1007/s10506-023-09361-y .

T. Igamberdiev, D. N. L. Vu, F. Künnecke, Z. Yu, J. Holmer, and I. Habernal, “DP-NMT: Scalable Differentially-Private Machine Translation.” 2023.

L. Held and I. Habernal, “LaCour!: Enabling Research on Argumentation in Hearings of the European Court of Human Rights.” 2023.

T. Igamberdiev and I. Habernal, “Privacy-Preserving Graph Convolutional Networks for Text Classification,” in Proceedings of the Thirteenth Language Resources and Evaluation Conference, 2022, pp. 338–350.

T. Igamberdiev, T. Arnold, and I. Habernal, “DP-Rewrite: Towards Reproducibility and Transparency in Differentially Private Text Rewriting,” in Proceedings of the 29th International Conference on Computational Linguistics, 2022, pp. 2927–2933.

I. Habernal, “How reparametrization trick broke differentially-private text representation learning,”10.18653/v1/2022.acl-short.87__2022, doi:10.18653/v1/2022.acl-short.87 .

I. Habernal, “When differential privacy meets NLP: The devil is in the detail,”10.18653/v1/2021.emnlp-main.114__2021, doi:10.18653/v1/2021.emnlp-main.114 .

M. Glockner, I. Habernal, and I. Gurevych, “Why do you think that? Exploring Faithful Sentence-Level Rationales Without Supervision,”10.18653/v1/2020.findings-emnlp.97__2020, doi:10.18653/v1/2020.findings-emnlp.97 .

I. Habernal, H. Wachsmuth, I. Gurevych, and B. Stein, “The Argument Reasoning Comprehension Task: Identification and            Reconstruction of Implicit Warrants,”10.18653/v1/n18-1175__2018, doi:10.18653/v1/n18-1175 .

I. Habernal, H. Wachsmuth, I. Gurevych, and B. Stein, “Before Name-Calling: Dynamics and Triggers of Ad Hominem Fallacies            in Web Argumentation,”10.18653/v1/n18-1036__2018, doi:10.18653/v1/n18-1036 .