Convex Optimisation-Based Privacy-Preserving Distributed Average Consensus in Wireless Sensor Networks

Convex Optimisation-Based Privacy-Preserving Distributed Average Consensus in Wireless Sensor Networks

Li, Qiongxiu (Aalborg University)
Heusdens, R. (TU Delft Signal Processing Systems)
Christensen, Mads Græsbøll (Aalborg University)

2020

In many applications of wireless sensor networks, it is important that the privacy of the nodes of the network be protected. Therefore, privacy-preserving algorithms have received quite some attention recently. In this paper, we propose a novel convex optimization-based solution to the problem of privacy-preserving distributed average consensus. The proposed method is based on the primal-dual method of multipliers (PDMM), and we show that the introduced dual variables of the PDMM will only converge in a certain subspace determined by the graph topology and will not converge in the orthogonal complement. These properties are exploited to protect the private data from being revealed to others. More specifically, the proposed algorithm is proven to be secure for both passive and eavesdropping adversary models. Finally, the convergence properties and accuracy of the proposed approach are demonstrated by simulations which show that the method is superior to the state-of-the-art.

Distributed average consensus
convex optimisation
primal-dual method of multipliers
privacy
wireless sensor networks

http://resolver.tudelft.nl/uuid:4c2757e0-f73e-49ff-93c0-b4d955f6eafc

https://doi.org/10.1109/ICASSP40776.2020.9053348

IEEE

978-1-5090-6632-2

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP): Proceedings

ICASSP 2020, 2020-05-04 → 2020-05-08, Barcelona, Spain

Accepted author manuscript

Institutional Repository

conference paper

© 2020 Qiongxiu Li, R. Heusdens, Mads Græsbøll Christensen