TY - GEN
T1 - Can social influence be exploited to compromise security
T2 - 11th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019
AU - Sarkar, Soumajyoti
AU - Shakarian, Paulo
AU - Armenta, Mika
AU - Sanchez, Danielle
AU - Lakkaraju, Kiran
N1 - Funding Information:
more likely when participants received social reinforcement from multiple neighbors in the social network as opposed to a single exposure. These studies focus on the effect of network structure on the dynamics of behavioral diffusion. Contrary to this, we quantify influence using only the number of signals temporally sent to a user irrespective of how the signals diffused to its neighbors prior to its own adoption. Acknowledgments. Some of the authors are supported through the ARO grant W911NF-15-1-0282. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy or the United States Government. We would like to thank Glory Emmanuel-Avina and Victoria Newton for their insights and assistance with preliminary experiment design.
Funding Information:
Some of the authors are supported through the ARO grant W911NF-15-1-0282. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy?s National Nuclear Security Administration under contract DE-NA0003525. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy or the United States Government. We would like to thank Glory Emmanuel-Avina and Victoria Newton for their insights and assistance with preliminary experiment design.
Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/8/27
Y1 - 2019/8/27
N2 - While social media enables users and organizations to obtain useful information about technology like software and security feature usage, it can also allow an adversary to exploit users by obtaining information from them or influencing them towards injurious decisions. Prior research indicates that security technology choices are subject to social influence and that these decisions are often influenced by the peer decisions and number of peers in a user’s network. In this study we investigated whether peer influence dictates users’ decisions by manipulating social signals from peers in an online, controlled experiment. Human participants recruited from Amazon Mechanical Turk played a multi-round game in which they selected a security technology from among six of differing utilities. We observe that at the end of the game, a strategy to expose users to high quantity of peer signals reflecting suboptimal choices, in the later stages of the game successfully influences users to deviate from the optimal security technology. This strategy influences almost 1.5 times the number of users with respect to the strategy where users receive constant low quantity of similar peer signals in all rounds of the game.
AB - While social media enables users and organizations to obtain useful information about technology like software and security feature usage, it can also allow an adversary to exploit users by obtaining information from them or influencing them towards injurious decisions. Prior research indicates that security technology choices are subject to social influence and that these decisions are often influenced by the peer decisions and number of peers in a user’s network. In this study we investigated whether peer influence dictates users’ decisions by manipulating social signals from peers in an online, controlled experiment. Human participants recruited from Amazon Mechanical Turk played a multi-round game in which they selected a security technology from among six of differing utilities. We observe that at the end of the game, a strategy to expose users to high quantity of peer signals reflecting suboptimal choices, in the later stages of the game successfully influences users to deviate from the optimal security technology. This strategy influences almost 1.5 times the number of users with respect to the strategy where users receive constant low quantity of similar peer signals in all rounds of the game.
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U2 - 10.1145/3341161.3343688
DO - 10.1145/3341161.3343688
M3 - Conference contribution
AN - SCOPUS:85078842454
T3 - Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019
SP - 593
EP - 596
BT - Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019
A2 - Spezzano, Francesca
A2 - Chen, Wei
A2 - Xiao, Xiaokui
PB - Association for Computing Machinery, Inc
Y2 - 27 August 2019 through 30 August 2019
ER -