LAUSR

Abstract 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.