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Social robots are a type of robotics that focuses on creating intelligent and embodied machines capable of interacting and communicating with humans in a socially acceptable manner. However, these robots' potential to capture user data, including emotions, biometrics, and behavioural habits, raises significant privacy concerns that could influence users' intention to use and trust social robots. Therefore, there is a pressing need to synthesize a privacy model that helps unravel the complex behavioural processes underlying current and future HRI technologies. This work aims to contribute to the growing body of privacy-friendly robot design by proposing comprehensive guidelines that enable the development of trustworthy and transparent social robots that respect user privacy. We have established a set of theoretical constructs to address people's concerns regarding privacy across four dimensions: physical, informational, psychological, and social. Finally, guidelines are provided in each construct to enhance transparency and trust through compliance with laws like GDPR.
Callander et al. (Mon,) studied this question.