  The distributed inference framework comprises of a group of spatially
distributed nodes which acquire observations about a phenomenon of interest.
Due to bandwidth and energy constraints, the nodes often quantize their
observations into a finite-bit local message before sending it to the fusion
center (FC). Based on the local summary statistics transmitted by nodes, the FC
makes a global decision about the presence of the phenomenon of interest. The
distributed and broadcast nature of such systems makes them quite vulnerable to
different types of attacks. This paper addresses the problem of secure
communication in the presence of eavesdroppers. In particular, we focus on
efficient mitigation schemes to mitigate the impact of eavesdropping. We
present an overview of the distributed inference schemes under secrecy
constraints and describe the currently available approaches in the context of
distributed detection and estimation followed by a discussion on avenues for
future research.
