  How intrusive does a life-saving user-monitoring application really need to
be? While most previous research was focused on analyzing mental state of users
from social media and smartphones, there is little effort towards protecting
user privacy in these analyses. A challenge in analyzing user behaviors is that
not only is the data multi-dimensional with a myriad of user activities but
these activities occur at varying temporal rates. The overarching question of
our work is: Given a set of sensitive user features, what is the minimum amount
of information required to group users with similar behavior? Furthermore, does
this user behavior correlate with their mental state? Towards answering those
questions, our contributions are two fold: we introduce the concept of privacy
surfaces that combine sensitive user data at different levels of intrusiveness.
As our second contribution, we introduce MIMiS, an unsupervised privacy-aware
framework that clusters users in a given privacy surface configuration to
homogeneous groups with respect to their temporal signature. In addition, we
explore the trade-off between intrusiveness and prediction accuracy. MIMiS
employs multi-set decomposition in order to deal with incompatible temporal
granularities in user activities. We extensively evaluate MIMiS on real data.
Across a variety of privacy surfaces, MIMiS identified groups that are highly
homogeneous with respect to self-reported mental health scores. Finally, we
conduct an in-depth exploration of the discovered clusters, identifying groups
whose behavior is consistent with academic deadlines.
