Designing Privacy-Preserving Visual Perception for Robot Navigation Based on User Privacy Preferences
This work addresses privacy issues for users of mobile service robots, but it is incremental as it builds on existing technical approaches by incorporating user preferences.
The paper tackles the problem of privacy concerns in robot visual navigation by proposing a user-centered approach, finding that users prefer visual abstractions and low-resolution mechanisms, with preferred resolution depending on privacy level and robot proximity.
Visual navigation is a fundamental capability of mobile service robots, yet the onboard cameras required for such navigation can capture privacy-sensitive information and raise user privacy concerns. Existing approaches to privacy-preserving navigation-oriented visual perception have largely been driven by technical considerations, with limited grounding in user privacy preferences. In this work, we propose a user-centered approach to designing privacy-preserving visual perception for robot navigation. To investigate how user privacy preferences can inform such design, we conducted two user studies. The results show that users prefer privacy-preserving visual abstractions and capture-time low-resolution preservation mechanisms: their preferred RGB resolution depends both on the desired privacy level and robot proximity during navigation. Based on these findings, we further derive a user-configurable distance-to-resolution privacy policy for privacy-preserving robot visual navigation.