Investigating User Perspectives on Differentially Private Text Privatization

• URL: http://arxiv.org/abs/2503.09338v1
• Date: Wed, 12 Mar 2025 12:33:20 GMT
• Title: Investigating User Perspectives on Differentially Private Text Privatization
• Authors: Stephen Meisenbacher, Alexandra Klymenko, Alexander Karpp, Florian Matthes,
• Abstract summary: This work investigates how factors of $textitscenario$, $textitdata sensitivity$, $textitmechanism type$, and $textitreason for data collection$ impact user preferences for text privatization.<n>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.
• Score: 81.59631769859004
• License: http://creativecommons.org/licenses/by/4.0/
• 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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