@inproceedings{62ffebdae8714a5a83b0ed175e235bbf,
title = "Privacy mechanisms for drones: Perceptions of drone controllers and bystanders",
abstract = "Drones pose privacy concerns such as surveillance and stalking. Many technology-based or policy-based mechanisms have been proposed to mitigate these concerns. However, it is unclear how drone controllers and bystanders perceive these mechanisms and whether people intend to adopt them. In this paper, we report results from two rounds of online survey with 169 drone controllers and 717 bystanders in the U.S. We identified respondents' perceived pros and cons of eight privacy mechanisms. We found that owner registration and automatic face blurring individually received most support from both controllers and bystanders. Our respondents also suggested using varied combinations of mechanisms under different drone usage scenarios, highlighting their context-dependent preferences. We outline a set of important questions for future privacy designs and public policies of drones. Copyright is held by the owner/author(s). Publication rights licensed to ACM.",
keywords = "Drone, Perceptions, Privacy mechanisms, UAS, UAV",
author = "Yaxing Yao and Huichuan Xia and Yun Huang and Yang Wang",
note = "Funding Information: We thank Xingzhi Guo and anonymous reviewers for their helpful feedback. This research is in part supported by an internal grant from Syracuse University. Publisher Copyright: {\textcopyright} 2017 ACM. Copyright: Copyright 2018 Elsevier B.V., All rights reserved.; 2017 ACM SIGCHI Conference on Human Factors in Computing Systems, CHI 2017 ; Conference date: 06-05-2017 Through 11-05-2017",
year = "2017",
month = may,
day = "2",
doi = "10.1145/3025453.3025907",
language = "English (US)",
series = "Conference on Human Factors in Computing Systems - Proceedings",
publisher = "Association for Computing Machinery",
pages = "6777--6788",
booktitle = "CHI 2017 - Proceedings of the 2017 ACM SIGCHI Conference on Human Factors in Computing Systems",
}