  Contact tracing is a well-established and effective approach for the
containment of the spread of infectious diseases. While Bluetooth-based contact
tracing method using phones has become popular recently, these approaches
suffer from the need for a critical mass adoption to be effective. In this
paper, we present WiFiTrace, a network-centric approach for contact tracing
that relies on passive WiFi sensing with no client-side involvement. Our
approach exploits WiFi network logs gathered by enterprise networks for
performance and security monitoring, and utilizes them for reconstructing
device trajectories for contact tracing. Our approach is specifically designed
to enhance the efficacy of traditional methods, rather than to supplant them
with new technology. We designed an efficient graph algorithm to scale our
approach to large networks with tens of thousands of users. The graph-based
approach outperforms an indexed PostgresSQL in memory by at least 4.5X without
any index update overheads or blocking. We have implemented a full prototype of
our system and deployed it on two large university campuses. We validated our
approach and demonstrate its efficacy using case studies and detailed
experiments using real-world WiFi datasets.
