Privacy Personas: Clustering Users via Attitudes and Behaviors toward Security Practices

A primary goal of research in usable security and privacy is to understand the differences and similarities between users. While past researchers have clustered users into different groups, past categories of users have proven to be poor predictors of end-user behaviors. In this paper, we perform an alternative clustering of users based on their behaviors. Through the analysis of data from surveys and interviews of participants, we identify five user clusters that emerge from end-user behaviors-Fundamentalists, Lazy Experts, Technicians, Amateurs and the Marginally Concerned. We examine the stability of our clusters through a survey-based study of an alternative sample, showing that clustering remains consistent. We conduct a small-scale design study to demonstrate the utility of our clusters in design. Finally, we argue that our clusters complement past work in understanding privacy choices, and that our categorization technique can aid in the design of new computer security technologies.