Health science researchers studying human behavior rely on wearable cameras to visually confirm behaviors in real-world settings. However, privacy concerns significantly impede their adoption. Lens orientation and activity-oriented cameras have potential in balancing the need to visually validate the wearers’ activities while reducing privacy concerns. To increase adoption and further alleviate privacy concerns while maintaining utility, generative stylizing approaches, like cartooning using generative adversarial networks (GANs), have recently shown promise. We investigate different cartoon-based obfuscation of activity-oriented footage through two studies. The first deploys crowdsourcing methods (n=60), while the second is experiential, where participants (n=49) don the device for an entire day and report concerns on their footage. Our findings support that cartoonization of activity-oriented data significantly reduces privacy concerns, particularly among bystanders in high privacy-concerning scenarios, while maintaining context verification (90% of participants). Through thematic analysis, we provide further insight for the community on best practices for cartoonization of activity-oriented videos.
Glenn Fernandes
Helen Zhu
Mahdi Pedram
Jacob Schauer
Soroush Shahi
Christopher Romano
Darren Gergle
Nabil Alshurafa
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