Enabling privacy-preserving first-person cameras using low-power sensors

Wearable smart devices such as smart-glasses, smart-watches and life-logging devices are becoming increasingly popular, and majority of them are being equipped with first-person cameras. Such first-person cameras on smart-glasses or lifeloggers capture photos/videos from user´s point of view, allowing them to record and share user´s everyday events. However, these wearable devices with first-person cameras raise serious privacy concerns because they can also capture extremely private moments and sensitive information of the user. Currently, such devices lack the intelligence to understand user´s preferences about certain scenarios being sensitive/private. To address this problem, we present PriFir, a scheme that enables Privacy-preserving First-person cameras. PriFir is based on the idea that low-power sensors (e.g. accelerometer, light sensor, etc.) embedded in smartphones and smart-watches can be leveraged to identify sensitive scenarios. Learning from user´s preferences, PriFir employs a cascade of classifiers that tags a scenario to be sensitive simply based on the characteristics of the low-power sensor data. We evaluate PriFir using real sensor traces spanning over multiple days and show that it performs highly accurate classification at a low energy cost.