文献类型：期刊文献

英文题名：Privacy Preserving via Secure Summation in Distributed Kalman Filtering

作者：Ding, Wenjie[1];Yang, Wen[1];Zhou, Jiayu[1];Shi, Ling[2];Chen, Guanrong[3]

机构：[1]East China Univ Sci & Technol, Shanghai 200237, Peoples R China;[2]Hong Kong Univ Sci & Technol, Dept Elect & Comp Engn, Kowloon, Hong Kong, Peoples R China;[3]City Univ Hong Kong, Ctr Chaos & Complex Networks, Hong Kong, Peoples R China

年份：2022

卷号：9

期号：3

起止页码：1481

外文期刊名：IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS

收录：;EI(收录号：20221011757448);WOS:【SCI-EXPANDED(收录号:WOS:000856122100040)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grants 62122026 and 61973123, in part by projects sponsored by the Programme of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017, in part by Shuguang Program supported by Shanghai Education Development Foundation and Shanghai Municipal Education Commission, and in part by the Fundamental Research Funds for the Central Universities. The work by L. Shi is supported by Hong Kong RGC General Research Fund under Grant 16206620.

语种：英文

外文关键词：Distributed Kalman filtering; multiparty computation; privacy preserving

摘要：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.