调研用户对文本差分隐私技术的接受度，发现输出质量影响隐私偏好。
Investigating User Perspectives on Differentially Private Text Privatization
- 通过全球721名普通人调查，分析场景、敏感度等因素对隐私需求的影响。
- 用户更关注隐私输出的语义连贯性和可用性，而非单纯加密。
- 揭示隐私技术落地需兼顾技术表现与用户心理，适合政策与设计参考。
近期研究中，差分隐私自然语言处理（DP NLP）发展迅速，尤其体现在差分隐私文本转换技术上——将潜在敏感输入文本在差分隐私保护下转换为可公开输出的文本，理想情况下既隐藏敏感信息又保留原意。尽管持续研究致力于解决该领域的开放挑战，但关于用户对该技术感知的研究仍十分匮乏，而这正是实际应用的关键障碍。本文对全球721名普通用户开展问卷调查，探究场景、数据敏感度、机制类型及数据收集理由等因素如何影响用户对文本隐私化的偏好。结果表明，尽管上述因素均有影响，但用户对隐私输出的可用性与语义连贯性极为敏感。研究揭示了在DP NLP研究中必须考虑的社会技术因素，为未来以用户为中心的深入探索打开通道。
原文摘要 · Abstract (English)
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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