  In this demonstration, we present a privacy-preserving epidemic surveillance
system. Recently, many countries that suffer from coronavirus crises attempt to
access citizen's location data to eliminate the outbreak. However, it raises
privacy concerns and may open the doors to more invasive forms of surveillance
in the name of public health. It also brings a challenge for privacy protection
techniques: how can we leverage people's mobile data to help combat the
pandemic without scarifying our location privacy. We demonstrate that we can
have the best of the two worlds by implementing policy-based location privacy
for epidemic surveillance. Specifically, we formalize the privacy policy using
graphs in light of differential privacy, called policy graph. Our system has
three primary functions for epidemic surveillance: location monitoring,
epidemic analysis, and contact tracing. We provide an interactive tool allowing
the attendees to explore and examine the usability of our system: (1) the
utility of location monitor and disease transmission model estimation, (2) the
procedure of contact tracing in our systems, and (3) the privacy-utility
trade-offs w.r.t. different policy graphs. The attendees can find that it is
possible to have the full functionality of epidemic surveillance while
preserving location privacy.
