  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.
