Adaptive Communication Networks with Privacy Guarantees
It addresses the problem of privacy preservation in communication networks from a control-theoretic perspective, but the results are presented without concrete numerical benchmarks, making the contribution incremental.
This paper develops an algorithm for network synthesis that maximizes node privacy by optimally selecting communication graph weights using observability, graph theory, and optimization. The adaptive network responds to intrusions by changing topology online to reduce information exposure.
Utilizing the concept of observability, in conjunction with tools from graph theory and optimization, this paper develops an algorithm for network synthesis with privacy guarantees. In particular, we propose an algorithm for the selection of optimal weights for the communication graph in order to maximize the privacy of nodes in the network, from a control theoretic perspective. In this direction, we propose an observability-based design of the communication topology that improves the privacy of the network in presence of an intruder. The resulting adaptive network responds to the intrusion by changing the topology of the network-in an online manner- in order to reduce the information exposed to the intruder.