@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 *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."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="meisenbacher-etal-2025-investigating">
<titleInfo>
<title>Investigating User Perspectives on Differentially Private Text Privatization</title>
</titleInfo>
<name type="personal">
<namePart type="given">Stephen</namePart>
<namePart type="family">Meisenbacher</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Alexandra</namePart>
<namePart type="family">Klymenko</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Alexander</namePart>
<namePart type="family">Karpp</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Florian</namePart>
<namePart type="family">Matthes</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-04</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Sixth Workshop on Privacy in Natural Language Processing</title>
</titleInfo>
<name type="personal">
<namePart type="given">Ivan</namePart>
<namePart type="family">Habernal</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sepideh</namePart>
<namePart type="family">Ghanavati</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Vijayanta</namePart>
<namePart type="family">Jain</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Timour</namePart>
<namePart type="family">Igamberdiev</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Shomir</namePart>
<namePart type="family">Wilson</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Albuquerque, New Mexico</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-246-6</identifier>
</relatedItem>
<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>
<identifier type="citekey">meisenbacher-etal-2025-investigating</identifier>
<identifier type="doi">10.18653/v1/2025.privatenlp-main.8</identifier>
<location>
<url>https://aclanthology.org/2025.privatenlp-main.8/</url>
</location>
<part>
<date>2025-04</date>
<extent unit="page">
<start>86</start>
<end>105</end>
</extent>
</part>
</mods>
</modsCollection>
%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