Research has demonstrated that users’ heuristic decision-making processes cause external factors like defaults and framing to influence the outcome of privacy decisions. Proponents of “privacy nudging” have proposed leveraging these effects to guide users’ decisions. Our research shows that defaults and framing not only influence the outcome of privacy decisions, but also the process of evaluating the contextual factors associated with the decision, effectively making the decision-making process more heuristic. In our analysis of an existing dataset of scenario-based smart home privacy decisions, we demonstrate that defaults and framing not only have a direct effect on participants’ decisions; they also moderate the effect of their cognitive appraisals of the presented scenarios on the decision. These results suggest that nudges like defaults and framing exacerbate the well-researched problem that people often employ heuristics rather than making deliberate privacy decisions, and that privacy-setting interfaces should avoid the effects of heuristic decision-making.

Abstract Smartphone users are often unaware of mobile applications’ (“apps”) third-party data collection and sharing practices, which put them at higher risk of privacy breaches. One way to raise awareness of these practices is by providing unobtrusive but pervasive visualizations that can be presented in a glanceable manner. In this paper, we applied Wogalter et al.’s Communication-Human Information Processing model (C-HIP) to design and prototype eight different visualizations that depict smartphone apps’ data sharing activities. We varied the granularity and type (i.e., data-centric or app-centric) of information shown to users and used the screensaver/lock screen as a design probe. Through interview-based design probes with Android users (n=15), we investigated the aspects of the data exposure visualizations that influenced users’ comprehension and privacy awareness. Our results shed light on how users’ perceptions of privacy boundaries influence their preference regarding the information structure of these visualizations, and the tensions that exist in these visualizations between glanceability and granularity. We discuss how a pervasive, soft paternalistic approach to privacy-related visualization may raise awareness by enhancing the transparency of information flow, thereby, unobtrusively increasing users’ understanding of data sharing practices of mobile apps. We also discuss implications for privacy research and glanceable security.

In this article, we extend and improve upon a previously developed data-driven approach to design privacy-setting interfaces for users of household IoT devices. The essence of this approach is to gather users’ feedback on household IoT scenarios before developing the interface, which allows us to create a navigational structure that preemptively maximizes users’ efficiency in expressing their privacy preferences, and develop a series of ‘privacy profiles’ that allow users to express a complex set of privacy preferences with the single click of a button. We expand upon the existing approach by proposing a more sophisticated translation of statistical results into interface design, and by extensively discussing and analyzing the tradeoff between user-model parsimony and accuracy in developing privacy profiles and default settings.

User testing is often used to inform the development of user interfaces (UIs). But what if an interface needs to be developed for a system that does not yet exist? In that case, existing datasets can provide valuable input for UI development. We apply a data-driven approach to the development of a privacy-setting interface for Internet-of-Things (IoT) devices. Applying machine learning techniques to an existing dataset of users' sharing preferences in IoT scenarios, we develop a set of "smart" default profiles. Our resulting interface asks users to choose among these profiles, which capture their preferences with an accuracy of 82%---a 14% improvement over a naive default setting and a 12% improvement over a single smart default setting for all users.