MIMiS: Minimally Intrusive Mining of Smartphone User Behaviors

The proliferation of smartphones has lead researchers towards using them as an observational tool in psychological science. However, there is little effort towards protecting user privacy in these analyses. The overarching question of our work is: Given a set of sensitive user features, what is the minimum amount of information required to group similar users? Our contributions are two fold: we introduce privacy surfaces that combine sensitive user data at different levels of temporal granularity. Second, we introduce MIMiS, an unsupervised privacy-aware framework that clusters users as homogeneous groups with respect to their temporal signature. In addition, we explore the trade-off between intrusiveness and prediction accuracy. We extensively evaluate MIMiS on real data across a variety of privacy surfaces. MIMiS identified groups that are highly homogeneous w.r.t. user mental health scores and their academic performance.