The influence of explanation designs on user understanding differential privacy and making data-sharing decision.

Differential privacy (DP) technologies are being promoted by organizations to encourage data sharing. However, without a proper understanding of how these technologies work, individuals may make incorrect data-sharing decisions. A design gap exists in effectively explaining the workings of DP technologies, such as Local DP, to users. Our research aimed to fill this gap by designing a visual explanatory illustration. We conducted an online survey with 228 participants to assess the impact of different explanation designs on understanding DP and data-sharing decisions. Our study found that the visual illustration was more effective than the text-based description in helping individuals comprehend Local DP's privacy protection against large organizations, with the illustration group exhibiting an increase of 51.4% in comprehension test scores. The study also found that improved knowledge of privacy-enhancing technologies does not guarantee willingness to share protected data. Building on our study insights, future research could explore integrating natural language processing technologies and incorporating behavioral intervention designs for more effective explanations of privacy protection, thereby preventing misinformed data-sharing decisions.