Understanding Privacy Risks and Perceived Benefits in Open Dataset Collection for Mobile Affective Computing

Abstract

Collecting large-scale mobile and wearable sensor datasets from daily contexts is essential in developing machine learning models for enabling everyday affective computing applications. However, there is a lack of knowledge on data contributors' perceived benefits and risks in participating in open dataset collection projects. To bridge this gap, we conducted an in-situ study on building an open dataset with mobile and wearable devices for affective computing research (N = 100, 4 weeks). Our study results showed that a mixture of financial and altruistic benefits was important in eliciting data contribution. Sensor-specific risks were largely associated with the revelation of personal traits and social behaviors. However, most of the participants were less concerned with open dataset collection and their perceived sensitivity of each sensor data did not change over time. We further discuss alternative approaches to promote data contributors' motivations and suggest design guidelines to alleviate potential privacy concerns in mobile open dataset collection.