Paper Info
Title
Privacy Preserving via Secure Summation in Distributed Kalman Filtering
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 requires that the estimate at each time instant is accurate, it brings more challenges to design privacy-preserving scheme for operation. In this article, we design a privacy-preserving scheme for distributed Kalman filtering without the loss of estimation performance, which is also suitable for average consensus or dynamic average consensus of multiagent systems. We first build a secure multihop communication based on an encryption scheme. We then calculate the sum of the states of neighboring nodes with secure summation, which ensures that the state update will not reveal the state of the node to its neighboring nodes. We employ different methods to calculate the sum of the states of neighboring nodes against noncollusive and collusive adversaries. For the noncollusive 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.
Year
DOI
Venue
2022
10.1109/TCNS.2022.3155109
IEEE Transactions on Control of Network Systems
Keywords
DocType
Volume
Distributed Kalman filtering,multiparty computation,privacy preserving
Journal
9
Issue
ISSN
Citations 
3
2325-5870
0
PageRank 
References 
Authors
0.34
21
5
Name
Order
Citations
PageRank
Wenjie Ding100.34
Wen Yang200.34
Jiayu Zhou300.34
Ling Shi41717107.86
Guanrong Chen500.34