Valuating Friends’ Privacy: Does Anonymity of Sharing Personal Data Matter?

Authors: 

Yu Pu, The Pennsylvania State University; Jens Grossklags, Technical University of Munich

Abstract: 

Through their third-party app installation decisions, users are frequently triggering interdependent privacy consequences by sharing personal information of their friends who are unable to control these information flows. With our study, we aim to quantify the value which app users attribute to their friends' information (i.e., value of interdependent privacy) and to understand how this valuation is affected by two factors: sharing anonymity (i.e., whether disclosure of friends' information is anonymous), and context relevance (i.e., whether friends' information is necessary for apps' functionality). Specifically, we conduct a between-subject, choice-based conjoint analysis study with 4 treatment conditions (2 sharing anonymity × 2 context relevance). Our study confirms the important roles that sharing anonymity and context relevance play in the process of interdependent privacy valuation. In addition, we also investigate how other factors, e.g., individuals' personal attributes and experiences, affect interdependent privacy valuations by applying structural equation modeling analysis. Our research findings yield design implications as well as contribute to policy discussions to better account for the problem of interdependent privacy.

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BibTeX
@inproceedings {205164,
author = {Yu Pu and Jens Grossklags},
title = {Valuating Friends{\textquoteright} Privacy: Does Anonymity of Sharing Personal Data Matter?},
booktitle = {Thirteenth Symposium on Usable Privacy and Security ({SOUPS} 2017)},
year = {2017},
isbn = {978-1-931971-39-3},
address = {Santa Clara, CA},
pages = {339--355},
url = {https://www.usenix.org/conference/soups2017/technical-sessions/presentation/pu},
publisher = {{USENIX} Association},
month = jul,
}

Presentation Audio