Privacy Risks of Robot Vision: A User Study on Image Modalities and Resolution
This addresses privacy risks for users in robotic applications, particularly in personal or sensitive environments, but is incremental as it builds on existing concerns with new user data.
The study investigated how different image modalities and resolutions affect user privacy concerns in robot vision, finding that depth and semantic segmentation images are broadly viewed as privacy-safe, and most participants consider 32*32 RGB images nearly privacy-preserving while 16*16 resolution fully guarantees privacy.
User privacy is a crucial concern in robotic applications, especially when mobile service robots are deployed in personal or sensitive environments. However, many robotic downstream tasks require the use of cameras, which may raise privacy risks. To better understand user perceptions of privacy in relation to visual data, we conducted a user study investigating how different image modalities and image resolutions affect users' privacy concerns. The results show that depth images are broadly viewed as privacy-safe, and a similarly high proportion of respondents feel the same about semantic segmentation images. Additionally, the majority of participants consider 32*32 resolution RGB images to be almost sufficiently privacy-preserving, while most believe that 16*16 resolution can fully guarantee privacy protection.