Maximiliane Windl, LMU Munich and Munich Center for Machine Learning (MCML); Jan Leusmann, LMU Munich; Albrecht Schmidt, LMU Munich and Munich Center for Machine Learning (MCML); Sebastian S. Feger, LMU Munich and Rosenheim Technical University of Applied Sciences; Sven Mayer, LMU Munich and Munich Center for Machine Learning (MCML)
IAPP SOUPS Privacy Award
Future domestic robots will become integral parts of our homes. They will have various sensors that continuously collect data and varying locomotion and interaction capabilities, enabling them to access all rooms and physically manipulate the environment. This raises many privacy concerns. We investigate how such concerns can be mitigated, using all possibilities enabled by the robot’s novel locomotion and interaction abilities. First, we found that privacy concerns increase with advanced locomotion and interaction capabilities through an online survey (N = 90). Second, we conducted three focus groups (N = 22) to construct 86 patterns to communicate the states of microphones, cameras, and the internet connectivity of domestic robots. Lastly, we conducted a large-scale online survey (N = 1720) to understand which patterns perform best regarding trust, privacy, understandability, notification qualities, and user preference. Our final set of communication patterns will guide developers and researchers to ensure a privacy-preserving future with domestic robots.
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author = {Maximiliane Windl and Jan Leusmann and Albrecht Schmidt and Sebastian S. Feger and Sven Mayer},
title = {Privacy Communication Patterns for Domestic Robots},
booktitle = {Twentieth Symposium on Usable Privacy and Security (SOUPS 2024)},
year = {2024},
isbn = {978-1-939133-42-7},
address = {Philadelphia, PA},
pages = {121--138},
url = {https://www.usenix.org/conference/soups2024/presentation/windl},
publisher = {USENIX Association},
month = aug
}