TY - GEN
T1 - Comparing hypothetical and realistic privacy valuations
AU - Tan, Joshua
AU - Sharif, Mahmood
AU - Bhagavatula, Sruti
AU - Beckerle, Matthias
AU - Mazurek, Michelle L.
AU - Bauer, Lujo
N1 - Publisher Copyright:
© 2018 Copyright held by the owner/author(s).
PY - 2018/10/15
Y1 - 2018/10/15
N2 - 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.
AB - 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.
KW - Human factors
KW - Online study
KW - Privacy economics
UR - http://www.scopus.com/inward/record.url?scp=85056908752&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85056908752&partnerID=8YFLogxK
U2 - 10.1145/3267323.3268961
DO - 10.1145/3267323.3268961
M3 - Conference contribution
AN - SCOPUS:85056908752
T3 - Proceedings of the ACM Conference on Computer and Communications Security
SP - 168
EP - 182
BT - WPES 2018 - Proceedings of the 2018 Workshop on Privacy in the Electronic Society, co-located with CCS 2018
PB - Association for Computing Machinery
T2 - 17th ACM Workshop on Privacy in the Electronic Society, WPES 2018, held in conjunction with the 25th ACM Conference on Computer and Communications Security, CCS 2018
Y2 - 15 October 2018
ER -