@inproceedings {pub3868,
	title = {Towards Privacy-preserving Personalized Social Robots By Enabling Dynamic Boundary Management},
	author = {Manuel Dietrich},
	year = {2019},
	abstract = {Designing personalized social robots, which should become an intrinsic part of everyday life, raises new challenges on how to respect the privacy of people interacting with them. In this paper, we introduce a little recognized conceptual perspective on privacy which, in our opinion, is highly relevant for the design of personalized social robots. This conceptual perspective, introduced by Irwin Altman (Altman 1975) highlights that in everyday social life, especially with respect to interpersonal interaction, privacy preferences are not static. Instead, they tend to be in constant change dependent on context and experience. What matters for privacy is the possibility to dynamically reconfigure the degree of disclosure of personal information - in Altman{\textquoteright}s words being able to continuously manage the boundaries. Since personalized robots, for instance in their role as companions or life assistants, can be seen as artificial social actors Altman{\textquoteright}s perspective becomes relevant for their design. Taking the conceptual thoughts into account leads to a privacy-design strategy, which discards common approaches of an a priori configuration of preferences and rather targets a selective disclosure of personal information as part of a continuous boundary management process. In this paper, we discuss how we apply this perspective into a design process for robots, which are able to maintain long-term interaction in a privacy-preserving way.},
	publisher = {PLOT-HRI},
	booktitle = {Proceedings of the Workshop on Personalization in Long-Term Human-Robot Interaction at the 2019 International Conference on Human-Robot Interaction},
	city = {Daegu, Korea}
}