Bayesian Evaluation of User App Choices in the Presence of Risk Communication on Android Devices
This addresses privacy and security concerns for mobile app users, though it is incremental in improving existing risk communication methods.
The study tackled the problem of users making insecure app choices by testing visual risk indicators on Android devices, finding that these indicators led to more risk-averse selections among 60 participants.
In the age of ubiquitous technologies, security- and privacy-focused choices have turned out to be a significant concern for individuals and organizations. Risks of such pervasive technologies are extensive and often misaligned with user risk perception, thus failing to help users in taking privacy-aware decisions. Researchers usually try to find solutions for coherently extending trust into our often inscrutable electronic networked environment. To enable security- and privacy-focused decision-making, we mainly focused on the realm of the mobile marketplace, examining how risk indicators can help people choose more secure and privacy-preserving apps. We performed a naturalistic experiment with N=60 participants, where we asked them to select applications on Android tablets with accurate real-time marketplace data. We found that, in aggregate, app selections changed to be more risk-averse in the presence of user risk-perception-aligned visual indicators. Our study design and research propose practical and usable interactions that enable more informed, risk-aware comparisons for individuals during app selections. We include an explicit argument for the role of human decision-making during app selection, beyond the current trend of using machine learning to automate privacy preferences after selection during run-time.