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Proper communication is key to the adoption and implementation ofdifferential privacy (DP). However, a prior study found that laypeople did notunderstand the data perturbation processes of DP and how DP noise protectstheir sensitive personal information. Consequently, they distrusted thetechniques and chose to opt out of participating. In this project, we designedexplanative illustrations of three DP models (Central DP, Local DP, ShufflerDP) to help laypeople conceptualize how random noise is added to protectindividuals' privacy and preserve group utility. Following pilot surveys andinterview studies, we conducted two online experiments (N = 595) examiningparticipants' comprehension, privacy and utility perception, and data-sharingdecisions across the three DP models. Besides the comparisons across the threemodels, we varied the noise levels of each model. We found that theillustrations can be effective in communicating DP to the participants. Givenan adequate comprehension of DP, participants preferred strong privacyprotection for a certain type of data usage scenarios (i.e., commercialinterests) at both the model level and the noise level. We also obtainedempirical evidence showing participants' acceptance of the Shuffler DP modelfor data privacy protection. Our findings have implications for multiplestakeholders for user-centered deployments of differential privacy, includingapp developers, DP model developers, data curators, and online users.

Using Illustrations to Communicate Differential Privacy Trust Models: An Investigation of Users' Comprehension, Perception, and Data Sharing Decision