Evaluating In-Workflow Messages for Improving Mental Models of End-to-End Encryption


Omer Akgul, Wei Bai, Shruti Das, and Michelle L. Mazurek, University of Maryland


As large messaging providers increasingly adopt end-to-end encryption, private communication is readily available to more users than ever before. However, misunderstandings of end-to-end encryption's benefits and shortcomings limit people's ability to make informed choices about how and when to use these services. This paper explores the potential of using short educational messages, built into messaging workflows, to improve users' functional mental models of secure communication. A preliminary survey study (n=461) finds that such messages, when used in isolation, can effectively improve understanding of several key concepts. We then conduct a longitudinal study (n=61) to test these messages in a more realistic environment: embedded into a secure messaging app. In this second study, we do not find statistically significant evidence of improvement in mental models; however, qualitative evidence from participant interviews suggests that if made more salient, such messages could have potential to improve users' understanding.

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@inproceedings {272120,
author = {Omer Akgul and Wei Bai and Shruti Das and Michelle L. Mazurek},
title = {Evaluating In-Workflow Messages for Improving Mental Models of End-to-End Encryption},
booktitle = {30th {USENIX} Security Symposium ({USENIX} Security 21)},
year = {2021},
isbn = {978-1-939133-24-3},
pages = {447--464},
url = {https://www.usenix.org/conference/usenixsecurity21/presentation/akgul},
publisher = {{USENIX} Association},
month = aug,

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