Using a Blocklist to Improve the Security of User Selection of Android Patterns


Collins W. Munyendo and Miles Grant, The George Washington University; Philipp Markert, Ruhr University Bochum; Timothy J. Forman, United States Navy; Adam J. Aviv, The George Washington University


Android patterns remain a popular method for unlocking smartphones, despite evidence suggesting that many users choose easily guessable patterns. In this paper, we explore the usage of blocklists to improve the security of user-chosen patterns by disallowing common patterns, a feature currently unavailable on Android but used by Apple during PIN selection. In a user study run on participants' smartphones (n = 1006), we tested 5 different blocklist sizes and compared them to a control treatment. We find that even the smallest blocklist (12 patterns) had benefits, reducing a simulated attacker's success rate after 30 guesses from 24 % to 20 %. The largest blocklist (581 patterns) reduced the percentage of correctly guessed patterns after 30 attempts down to only 2 %. In terms of usability, blocklists had limited negative impact on short-term recall rates and entry times, with reported SUS values indicating reasonable usability when selecting patterns in the presence of a blocklist. Based on our simulated attacker performance results for different blocklist sizes, we recommend blocking 100 patterns for a good balance between usability and security.

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@inproceedings {274417,
author = {Collins W. Munyendo and Miles Grant and Philipp Markert and Timothy J. Forman and Adam J. Aviv},
title = {Using a Blocklist to Improve the Security of User Selection of Android Patterns},
booktitle = {Seventeenth Symposium on Usable Privacy and Security (SOUPS 2021)},
year = {2021},
isbn = {978-1-939133-25-0},
pages = {37--56},
url = {},
publisher = {USENIX Association},
month = aug

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