Personal data analyses, for instance, in the area of eHealth, can provide many benefits while posing privacy challenges at the same time. Applying differentially private mechanisms have become one of the dominant approaches for the protection of personal data in statistical analyses. Transparency of the privacy functionality of differentially private mechanisms can facilitate informed decisionmaking for using differentially private systems and understanding the privacy consequences of such decisions. However, differential privacy is a complex concept that makes it a challenge to explain the privacy functionality it comprises to lay users. Our research outlined in this vision paper aims to address this challenge in three phases by creating and analysing metaphors for conveying the functionality of differential privacy to lay data subjects who should decide about sharing their data in the context of differentially private data analysis. In this paper, we report the results of the first two phases of our study for extracting the metaphors and adapting and extending them based on two rounds of analytical evaluations and feedback from privacy experts. Further, we briefly discuss how, in the third phase, we want to move forward and empirically assess the resulted metaphors from previous steps by involving lay users.
CCS CONCEPTS• Security and privacy → Human and societal aspects of security and privacy; • Human-centered computing → Empirical studies in HCI.