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.
翻译:然而,先前的一项研究发现,外行人并不理解DP的数据扰动过程以及DP噪音如何保护其敏感的个人信息。因此,他们不信任技术,选择不参与。在这个项目中,我们设计了三种DP模型(中央DP、地方DP、Shuffler DP)的推断图解,以帮助外行人构思如何增加随机噪音以保护个人隐私和维护群体效用。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),对参与者的理解、隐私和公用事业观念以及三个DP模式的数据共享决定进行了审查。除了三个模型的比较之外,我们改变了每种模型的噪音水平。我们发现,这些图解可以有效地向参与者传达DP。鉴于DP的充分理解,与会者倾向于在模型一级和噪音层面对某种类型的数据使用情景(即商业利益)进行强有力的隐私保护。我们还获得了两个实验性证据,表明参与者接受Shuffler DP模型模型,在数据隐私保护方面接受Shuffler DP模型,我们的调查结果对多种用户的用户(包括发展者)的在线应用影响。