@inproceedings{meisenbacher-etal-2025-investigating,
title = "Investigating User Perspectives on Differentially Private Text Privatization",
author = "Meisenbacher, Stephen and
Klymenko, Alexandra and
Karpp, Alexander and
Matthes, Florian",
editor = "Habernal, Ivan and
Ghanavati, Sepideh and
Jain, Vijayanta and
Igamberdiev, Timour and
Wilson, Shomir",
booktitle = "Proceedings of the Sixth Workshop on Privacy in Natural Language Processing",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.privatenlp-main.8/",
doi = "10.18653/v1/2025.privatenlp-main.8",
pages = "86--105",
ISBN = "979-8-89176-246-6",
abstract = "Recent literature has seen a considerable uptick in \textit{Differentially Private Natural Language Processing} (DP NLP). This includes DP text privatization, where potentially sensitive input texts are transformed under DP to achieve privatized output texts that ideally mask sensitive information \textit{and} maintain original semantics. Despite continued work to address the open challenges in DP text privatization, there remains a scarcity of work addressing user perceptions of this technology, a crucial aspect which serves as the final barrier to practical adoption. In this work, we conduct a survey study with 721 laypersons around the globe, investigating how the factors of \textit{scenario}, \textit{data sensitivity}, \textit{mechanism type}, and \textit{reason for data collection} impact user preferences for text privatization. We learn that while all these factors play a role in influencing privacy decisions, users are highly sensitive to the utility and coherence of the private output texts. Our findings highlight the socio-technical factors that must be considered in the study of DP NLP, opening the door to further user-based investigations going forward."
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<abstract>Recent literature has seen a considerable uptick in Differentially Private Natural Language Processing (DP NLP). This includes DP text privatization, where potentially sensitive input texts are transformed under DP to achieve privatized output texts that ideally mask sensitive information and maintain original semantics. Despite continued work to address the open challenges in DP text privatization, there remains a scarcity of work addressing user perceptions of this technology, a crucial aspect which serves as the final barrier to practical adoption. In this work, we conduct a survey study with 721 laypersons around the globe, investigating how the factors of scenario, data sensitivity, mechanism type, and reason for data collection impact user preferences for text privatization. We learn that while all these factors play a role in influencing privacy decisions, users are highly sensitive to the utility and coherence of the private output texts. Our findings highlight the socio-technical factors that must be considered in the study of DP NLP, opening the door to further user-based investigations going forward.</abstract>
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%0 Conference Proceedings
%T Investigating User Perspectives on Differentially Private Text Privatization
%A Meisenbacher, Stephen
%A Klymenko, Alexandra
%A Karpp, Alexander
%A Matthes, Florian
%Y Habernal, Ivan
%Y Ghanavati, Sepideh
%Y Jain, Vijayanta
%Y Igamberdiev, Timour
%Y Wilson, Shomir
%S Proceedings of the Sixth Workshop on Privacy in Natural Language Processing
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-246-6
%F meisenbacher-etal-2025-investigating
%X Recent literature has seen a considerable uptick in Differentially Private Natural Language Processing (DP NLP). This includes DP text privatization, where potentially sensitive input texts are transformed under DP to achieve privatized output texts that ideally mask sensitive information and maintain original semantics. Despite continued work to address the open challenges in DP text privatization, there remains a scarcity of work addressing user perceptions of this technology, a crucial aspect which serves as the final barrier to practical adoption. In this work, we conduct a survey study with 721 laypersons around the globe, investigating how the factors of scenario, data sensitivity, mechanism type, and reason for data collection impact user preferences for text privatization. We learn that while all these factors play a role in influencing privacy decisions, users are highly sensitive to the utility and coherence of the private output texts. Our findings highlight the socio-technical factors that must be considered in the study of DP NLP, opening the door to further user-based investigations going forward.
%R 10.18653/v1/2025.privatenlp-main.8
%U https://aclanthology.org/2025.privatenlp-main.8/
%U https://doi.org/10.18653/v1/2025.privatenlp-main.8
%P 86-105
Markdown (Informal)
[Investigating User Perspectives on Differentially Private Text Privatization](https://aclanthology.org/2025.privatenlp-main.8/) (Meisenbacher et al., PrivateNLP 2025)
ACL