CyLab Student Seminar
- Robert Mehrabian Collaborative Innovation Center
- jOSH TAN
- Ph.D. Student
- Ph.D. Program in Societal Computing, Institute for Software Research
- Carnegie Mellon University
Comparing hypothetical and realistic privacy valuations
To protect users’ privacy, it is important to understand how they value personal information. Prior work identified how framing effects alter users’ valuations and highlighted the difficulty in eliciting real valuations through user studies under hypothetical circumstances. However, our understanding of users’ valuations remains limited to specific entities, information types, and levels of realism. We examined the effects of realism and purpose of use on users’ valuations of their personal information. Specifically, we conducted an online study in which participants (N=434) were asked to assign monetary value to their personal information in the context of an information marketplace involving different receiving parties, while we experimentally manipulated the level of realism of the scenario and the timing of eliciting valuations. Among our findings is a nuanced understanding of valuation biases, including when they may not apply. For example, we find that, contrary to common belief, participants’ valuations are not generally higher in hypothetical scenarios compared to realistic ones. Importantly, we find that while absolute valuations vary greatly between participants, the order in which users prioritize information types (i.e., users’ relative valuations of different attributes) remains stable across the levels of realism we study. We discuss how our findings inform system design and future studies.
Josh Tan is a PhD student in the Institute for Software Research, advised by Lorrie Cranor and Lujo Bauer. His research interests are in usable privacy and security, with a focus on understanding users' privacy preferences and helping users create strong passwords.
This is a practice talk for presentation at WPES 2018.
Lunch will be served.