  In this paper, we propose a compositional approach to construct
opacity-preserving finite abstractions (a.k.a symbolic models) for networks of
discrete-time nonlinear control systems. Particularly, we introduce new notions
of simulation functions that characterize the distance between control systems
while preserving opacity properties across them. Instead of treating
large-scale systems in a monolithic manner, we develop a compositional scheme
to construct the interconnected finite abstractions together with the overall
opacity-preserving simulation functions. For a network of incrementally
input-to-state stable control systems and under some small-gain type condition,
an algorithm for designing local quantization parameters is presented to
orderly build the local symbolic models of subsystems such that the network of
symbolic models simulates the original network for an a-priori defined accuracy
while preserving its opacity properties.
