  In this paper, we consider the problem of privacy preservation in the average
consensus problem when communication among nodes is quantized. More
specifically, we consider a setting where some nodes in the network are curious
but not malicious and they try to identify the initial states of other nodes
based on the data they receive during their operation (without interfering in
the computation in any other way), while some nodes in the network want to
ensure that their initial states cannot be inferred exactly by the curious
nodes. We propose two privacy-preserving event-triggered quantized average
consensus algorithms that can be followed by any node wishing to maintain its
privacy and not reveal the initial state it contributes to the average
computation. Every node in the network (including the curious nodes) is allowed
to execute a privacy-preserving algorithm or its underlying average consensus
algorithm. Under certain topological conditions, both algorithms allow the
nodes who adopt privacypreserving protocols to preserve the privacy of their
initial quantized states and at the same time to obtain, after a finite number
of steps, the exact average of the initial states.
