Although the NLP community has adopted central differential privacy as a go-to framework for privacy-preserving model training or data sharing, the choice and interpretation of the key parameter, privacy budget $\varepsilon$ that governs the strength of privacy protection, remains largely arbitrary. We argue that determining the $\varepsilon$ value should not be solely in the hands of researchers or system developers, but must also take into account the actual people who share their potentially sensitive data. In other words: Would you share your instant messages for $\varepsilon$ of 10? We address this research gap by designing, implementing, and conducting a behavioral experiment (311 lay participants) to study the behavior of people in uncertain decision-making situations with respect to privacy-threatening situations. Framing the risk perception in terms of two realistic NLP scenarios and using a vignette behavioral study help us determine what $\varepsilon$ thresholds would lead lay people to be willing to share sensitive textual data - to our knowledge, the first study of its kind.

翻译：尽管自然语言处理（NLP）社区已采用中心化差分隐私作为保护隐私模型训练或数据共享的标准框架，但关键参数——隐私预算（决定隐私保护强度的ε值）的选择和解释仍很大程度上具有随意性。我们主张，ε值的确定不应仅由研究人员或系统开发者决定，还必须考虑实际共享潜在敏感数据的用户。换言之：你会为了ε值为10而分享你的即时消息吗？为填补这一研究空白，我们设计、实施并开展了一项行为实验（311名普通参与者），研究人们在面对隐私威胁时的不确定决策行为。通过将风险感知置于两个真实NLP场景的框架下，并采用情景行为研究法，我们得以确定促使普通人愿意共享敏感文本数据的ε阈值——据我们所知，这是该领域的首项此类研究。