论文标题
使用插图传达差异隐私信任模型：对用户的理解，感知和数据共享决策的调查
Using Illustrations to Communicate Differential Privacy Trust Models: An Investigation of Users' Comprehension, Perception, and Data Sharing Decision
论文作者
论文摘要
正确的通信是采用和实施差异隐私（DP）的关键。但是，一项先前的研究发现，外行不了解DP的数据扰动过程以及DP噪声如何保护其敏感的个人信息。因此，他们不信任这些技术，并选择退出参与。在这个项目中，我们设计了三种DP模型（中央DP，本地DP，Shuffler DP）的解释性插图，以帮助外行人概念化如何添加随机噪声以保护个人的隐私并保留团体效用。在试点调查和访谈研究之后，我们进行了两个在线实验（n = 595），研究了参与者在三种DP模型中的理解，隐私和实用性感知以及数据共享决策。除了在这三个模型上进行比较外，我们还改变了每个模型的噪声水平。我们发现这些插图可以有效地将DP传达给参与者。如果对DP有足够的理解，参与者在模型级别和噪声级别上都偏爱某种类型的数据使用方案（即商业利益）的强大隐私保护。我们还获得了经验证据，表明参与者接受Shuffler DP模型以保护数据隐私。我们的发现对多个利益相关者对以用户为中心的差异隐私部署有影响，包括应用程序开发人员，DP模型开发人员，数据策展人和在线用户。
Proper communication is key to the adoption and implementation of differential privacy (DP). However, a prior study found that laypeople did not understand the data perturbation processes of DP and how DP noise protects their sensitive personal information. Consequently, they distrusted the techniques and chose to opt out of participating. In this project, we designed explanative illustrations of three DP models (Central DP, Local DP, Shuffler DP) to help laypeople conceptualize how random noise is added to protect individuals' privacy and preserve group utility. Following pilot surveys and interview studies, we conducted two online experiments (N = 595) examining participants' comprehension, privacy and utility perception, and data-sharing decisions across the three DP models. Besides the comparisons across the three models, we varied the noise levels of each model. We found that the illustrations can be effective in communicating DP to the participants. Given an adequate comprehension of DP, participants preferred strong privacy protection for a certain type of data usage scenarios (i.e., commercial interests) at both the model level and the noise level. We also obtained empirical evidence showing participants' acceptance of the Shuffler DP model for data privacy protection. Our findings have implications for multiple stakeholders for user-centered deployments of differential privacy, including app developers, DP model developers, data curators, and online users.
