Publications
Below is a searchable list of publications by the projects of the Priority Program.
Saad, Alia; Winterhalter, Verena; Strauss, Marvin; Schneegass, Stefan
“I Feel More Worried About My Privacy” Public Perceptions of Biometric Traces in Everyday Interactions Proceedings Article
In: Proceedings of the Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems, Association for Computing Machinery, New York, NY, USA, 2026, ISBN: 9798400722813.
Abstract | Links | BibTeX | Tags: behavioral biometrics, biometric traces, privacy risks, usable privacy, user awareness
@inproceedings{10.1145/3772363.3798601,
title = {"I Feel More Worried About My Privacy" Public Perceptions of Biometric Traces in Everyday Interactions},
author = {Alia Saad and Verena Winterhalter and Marvin Strauss and Stefan Schneegass},
url = {https://doi.org/10.1145/3772363.3798601},
doi = {10.1145/3772363.3798601},
isbn = {9798400722813},
year = {2026},
date = {2026-01-01},
booktitle = {Proceedings of the Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
series = {CHI EA '26},
abstract = {People leave behind biometric traces through everyday interactions, often without fully understanding their implications. While biometric technologies increasingly rely on subtle behavioral and interaction-based signals, little is known about how people perceive these traces in daily life. We present findings from an exploratory online study (N = 120) that used short, scenario-based videos to illustrate situations in which biometric traces may be inadvertently exposed, including fingerprints, gait, thermal residues, and interaction patterns in virtual environments. We examine which traces people recognize, how concerned they are about potential misuse, and how brief exposure to such scenarios shapes privacy perception. Results show that awareness and concern frequently diverge. Participants were familiar with visible, well-known biometrics, yet less aware of emerging or interaction-borne traces. Importantly, exposure to the scenarios prompted several participants to reconsider their privacy assumptions.},
keywords = {behavioral biometrics, biometric traces, privacy risks, usable privacy, user awareness},
pubstate = {published},
tppubtype = {inproceedings}
}
Saad, Alia; Pascher, Max; Kassem, Khaled; Heger, Roman; Liebers, Jonathan; Schneegass, Stefan; Gruenefeld, Uwe
Hand-in-Hand: Investigating Mechanical Tracking for User Identification in Cobot Interaction Proceedings Article
In: Proceedings of the 22nd International Conference on Mobile and Ubiquitous Multimedia, pp. 1–9, Association for Computing Machinery, <conf-loc>, <city>Vienna</city>, <country>Austria</country>, </conf-loc>, 2023, ISBN: 9798400709210.
Abstract | Links | BibTeX | Tags: behavioral biometrics, cobots, human-robot collaboration, human-robot interaction
@inproceedings{10.1145/3626705.3627771,
title = {Hand-in-Hand: Investigating Mechanical Tracking for User Identification in Cobot Interaction},
author = {Alia Saad and Max Pascher and Khaled Kassem and Roman Heger and Jonathan Liebers and Stefan Schneegass and Uwe Gruenefeld},
url = {https://doi.org/10.1145/3626705.3627771},
doi = {10.1145/3626705.3627771},
isbn = {9798400709210},
year = {2023},
date = {2023-01-01},
booktitle = {Proceedings of the 22nd International Conference on Mobile and Ubiquitous Multimedia},
pages = {1–9},
publisher = {Association for Computing Machinery},
address = {<conf-loc>, <city>Vienna</city>, <country>Austria</country>, </conf-loc>},
series = {MUM '23},
abstract = {Robots play a vital role in modern automation, with applications in manufacturing and healthcare. Collaborative robots integrate human and robot movements. Therefore, it is essential to ensure that interactions involve qualified, and thus identified, individuals. This study delves into a new approach: identifying individuals through robot arm movements. Different from previous methods, users guide the robot, and the robot senses the movements via joint sensors. We asked 18 participants to perform six gestures, revealing the potential use as unique behavioral traits or biometrics, achieving F1-score up to 0.87, which suggests direct robot interactions as a promising avenue for implicit and explicit user identification.},
keywords = {behavioral biometrics, cobots, human-robot collaboration, human-robot interaction},
pubstate = {published},
tppubtype = {inproceedings}
}