Self-driving cars and data collection: Privacy perceptions of networked autonomous vehicles


Cara Bloom, Joshua Tan, Javed Ramjohn, and Lujo Bauer, Carnegie Mellon University


Self-driving vehicles and other networked autonomous robots use sophisticated sensors to capture continuous data about the surrounding environment. In the public spaces where autonomous vehicles operate there is little reasonable expectation of privacy and no notice or choice given, raising privacy questions. To improve the acceptance of networked autonomous vehicles and to facilitate the development of technological and policy mechanisms to protect privacy, public expectations and concerns must first be investigated. In a study (n=302) of residents in cities with and without Uber autonomous vehicle fleets, we explore people's conceptions of the sensing and analysis capabilities of self-driving vehicles; their comfort with the different capabilities; and the effort, if any, to which they would be willing to go to opt out of data collection. We find that 54% of participants would spend more than five minutes using an online system to opt out of identifiable data collection. In addition, secondary use scenarios such as recognition, identification, and tracking of individuals and their vehicles were associated with low likelihood ratings and high discomfort. Surprisingly, those who thought secondary use scenarios were more likely were more comfortable with those scenarios. We discuss the implications of our results for understanding the unique challenges of this new technology and recommend industry guidelines to protect privacy.

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@inproceedings {205168,
author = {Cara Bloom and Joshua Tan and Javed Ramjohn and Lujo Bauer},
title = {Self-driving cars and data collection: Privacy perceptions of networked autonomous vehicles},
booktitle = {Thirteenth Symposium on Usable Privacy and Security ({SOUPS} 2017)},
year = {2017},
isbn = {978-1-931971-39-3},
address = {Santa Clara, CA},
pages = {357--375},
url = {},
publisher = {{USENIX} Association},