Effects of Knowledge and Experience on Privacy Decision-Making in Connected Autonomous Vehicle Scenarios

Abstract

To enhance the acceptance of connected autonomous vehicles (CAVs) and facilitate designs to protect people's privacy, it is essential to evaluate how people perceive the data collection and use inside and outside the CAVs and investigate effective ways to help them make informed privacy decisions. We conducted an online survey (N = 381) examining participants' utility-privacy tradeoff and data-sharing decisions in different CAV scenarios. Interventions that may encourage safer data-sharing decisions were also evaluated relative to a control. Results showed that the feedback intervention was effective in enhancing participants' knowledge of possible inferences of personal information in the CAV scenarios. Consequently, it helped participants make more conservative data-sharing decisions. We also measured participants' prior experience with connectivity and driver-assistance technologies and obtained its influence on their privacy decisions. We discuss the implications of the results for usable privacy design for CAVs.