Adaptive Communication Networks with Privacy Guarantees
arXiv:1704.01188 · doi:10.23919/ACC.2017.7963642
Abstract
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.
American Control Conference, 2017
References in corpus (1)
Cited by in corpus (9)
- A system-theoretic framework for privacy preservation in continuous-time multiagent dynamics
- Privacy-Preserving Push-Pull Method for Decentralized Optimization via State Decomposition
- Information-Theoretic Privacy in Distributed Average Consensus
- Privacy-preserving Decentralized Optimization via Decomposition
- ADMM Based Privacy-preserving Decentralized Optimization
- Privacy-Preserving Push-sum Average Consensus via State Decomposition
- Dynamics Based Privacy Protection for Average Consensus on Directed Graphs
- Secure and Privacy-Preserving Consensus
- Privacy-Preserving Average Consensus via State Decomposition