Social robots as companions play an increasingly important role in our everyday life. However, reaching the full
potential of social robots and the interaction between humans and robots requires permanent collection and processing of personal
data of users, e.g. video and audio data for image and speech recognition. In order to foster user acceptance, trust and to
address legal requirements as the General Data Protection Regulation of the EU, privacy needs to be integrated in the design
process of social robots. The Privacy by Design approach by Cavoukian indicates the relevance of a privacy-respecting development
and outlines seven abstract principle.In this paper two methods as a hands-on guideline to fulfill the principles are presented and discussed in the
content of the Privacy by Design approach. Privacy risks of a typical robot scenario are identified, analyzed and solutions are
proposed on the basis of the seven types of privacy and the privacy protection goals.
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2023. Proceedings of the 2023 ACM/IEEE International Conference on Human-Robot Interaction, ► pp. 331 ff.
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2021. Privacy framework for context-aware robot development. Paladyn, Journal of Behavioral Robotics 12:1 ► pp. 468 ff.
2021. “I still need my privacy”: Exploring the level of comfort and privacy preferences of German-speaking older adults in the case of mobile assistant robots. Pervasive and Mobile Computing 74 ► pp. 101397 ff.
Song, Bo, Hongda Xu, Wenna Hu, Yi Li & Yingzhi Guo
2023. How to calculate privacy: privacy concerns and service robots’ use intention in hospitality. Current Issues in Tourism► pp. 1 ff.
Song, Bo, Meng Zhang & Peipei Wu
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