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    <abstract>The field of privacy-preserving Natural Language Processing has risen in popularity, particularly at a time when concerns about privacy grow with the proliferation of large language models. One solution consistently appearing in recent literature has been the integration of Differential Privacy (DP) into NLP techniques. In this paper, we take these approaches into critical view, discussing the restrictions that DP integration imposes, as well as bring to light the challenges that such restrictions entail. To accomplish this, we focus on **DP-Prompt**, a recent method for text privatization leveraging language models to rewrite texts. In particular, we explore this rewriting task in multiple scenarios, both with DP and without DP. To drive the discussion on the merits of DP in NLP, we conduct empirical utility and privacy experiments. Our results demonstrate the need for more discussion on the usability of DP in NLP and its benefits over non-DP approaches.</abstract>
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%0 Conference Proceedings
%T Thinking Outside of the Differential Privacy Box: A Case Study in Text Privatization with Language Model Prompting
%A Meisenbacher, Stephen
%A Matthes, Florian
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F meisenbacher-matthes-2024-thinking
%X The field of privacy-preserving Natural Language Processing has risen in popularity, particularly at a time when concerns about privacy grow with the proliferation of large language models. One solution consistently appearing in recent literature has been the integration of Differential Privacy (DP) into NLP techniques. In this paper, we take these approaches into critical view, discussing the restrictions that DP integration imposes, as well as bring to light the challenges that such restrictions entail. To accomplish this, we focus on **DP-Prompt**, a recent method for text privatization leveraging language models to rewrite texts. In particular, we explore this rewriting task in multiple scenarios, both with DP and without DP. To drive the discussion on the merits of DP in NLP, we conduct empirical utility and privacy experiments. Our results demonstrate the need for more discussion on the usability of DP in NLP and its benefits over non-DP approaches.
%R 10.18653/v1/2024.emnlp-main.324
%U https://aclanthology.org/2024.emnlp-main.324
%U https://doi.org/10.18653/v1/2024.emnlp-main.324
%P 5656-5665

Markdown (Informal)

[Thinking Outside of the Differential Privacy Box: A Case Study in Text Privatization with Language Model Prompting](https://aclanthology.org/2024.emnlp-main.324) (Meisenbacher & Matthes, EMNLP 2024)

• Thinking Outside of the Differential Privacy Box: A Case Study in Text Privatization with Language Model Prompting (Meisenbacher & Matthes, EMNLP 2024)

ACL

• Stephen Meisenbacher and Florian Matthes. 2024. Thinking Outside of the Differential Privacy Box: A Case Study in Text Privatization with Language Model Prompting. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 5656–5665, Miami, Florida, USA. Association for Computational Linguistics.