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The design of social media interfaces greatly shapes how much, and when, people decide to reveal private information. For example, a designer can highlight a new system feature (e.g., your travel history displayed on a map) and show which friends are using this new addition. By making it seem as if sharing is the norm -- after all, your friends are doing it -- the designer signals to the end-user that he can and should participate and share information. This research focuses on two broad themes: what are the effects of design choices on changing what users think is appropriate to share and with whom? and how do norms interact with design to impact these decisions? Understanding how disclosure decisions are made and manipulated is critical as corporate and individual interests can be quite different. This is because norm-shaping can be used for benevolent purposes, such as guiding the end-user through an unfamiliar interface, but can also be used to manipulate the end-user and cause him or her to share information he or she would have preferred to keep private. The fact that such design patterns can be used both ways makes them particularly interesting: the user has no way of inferring the designer's intent, and policy makers and well intentioned designers have no mechanism for assessing the norm-shaping properties of their design choices. This research contributes to the development of tools to study user interfaces as embodiments of social norms as well as contributing more broadly to the discourse of privacy and sharing online.
The specific research goals are to (a) identify design patterns that shape disclosure norms, (b) experimentally determine the mechanisms by which they work (e.g., how patterns modify perception of norms and thus behavior), and (c) integrate these observations into existing theoretical frameworks (e.g., the "privacy calculus") that model how disclosure decisions are made. The PIs plan to use experiments to identify the impact of design on the perception of social norms and subsequent information divulging behavior. The experiments combine methodologies from experimental economics with Human Computer Interaction (HCI) methods. Additionally, the PIs will test econometrically an extension of the privacy calculus model that includes a preference for norm compliance, estimating an individual's willingness to trade-off between privacy preserving behavior and compliance with sharing norms. This research will demonstrate how tools from different disciplines can be used to enhance understanding of design in privacy and HCI. The results would feed back to the privacy, economics, and HCI communities.
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STARSS: Small: Side-Channel Analysis and Resiliency Targeting Accelerators