Privacy Preserving via Secure Summation in Distributed Kalman Filtering

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

3 Scopus Citations
View graph of relations

Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)1481-1492
Journal / PublicationIEEE Transactions on Control of Network Systems
Volume9
Issue number3
Online published28 Feb 2022
Publication statusPublished - Sep 2022

Abstract

Average consensus is a major operation in distributed Kalman filtering. It requires neighboring nodes to exchange state information with each other, which may result in undesirable private data leakage. Since distributed Kalman filtering demands accurate estimate at each time instant, which brings more challenges to design a privacy-preserving scheme for the operation. In this paper, we design a privacy-preserving scheme for distributed Kalman filtering without loss of estimation performance, which is also suitable for average consensus or dynamic average consensus of multi-agent systems. We first build a secure multi-hop communication based on an encryption scheme. We then calculate the sum of states of neighboring nodes with secure summation, which ensures the state update will not reveal the state of node to its neighboring nodes. We employ different methods to calculate the sum of the states of neighboring nodes against non-collusive and collusive adversaries. For the non-collusive case, the privacy of the honest nodes is preserved. For the collusive case, if there are too many adversaries, the privacy of the honest nodes could be exposed when accurate distributed Kalman filtering is accomplished. Therefore, we measure the risk of the global system suffering privacy leakage as the privacy index and improve the ability of the system to defend the collusive adversaries by using a group-based method. Some numerical examples are provided to illustrate the effectiveness of the proposed schemes.

Research Area(s)

  • Convergence, Distributed databases, Distributed Kalman Filtering, Heuristic algorithms, Kalman filters, Multi-Party Computation, Network systems, Peer-to-peer computing, Privacy, Privacy-Preserving

Citation Format(s)

Privacy Preserving via Secure Summation in Distributed Kalman Filtering. / Wenjie, Ding; Yang, Wen; Zhou, Jiayu; Shi, Ling; Chen, Guanrong.

In: IEEE Transactions on Control of Network Systems, Vol. 9, No. 3, 09.2022, p. 1481-1492.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review