Distributed Privacy-Preserving Fusion Estimation Using Homomorphic Encryption

被引:0
|
作者
Xinhao Yan [1 ,2 ]
Siqin Zhuo [1 ]
Yancheng Wu [3 ]
Bo Chen [1 ,2 ]
机构
[1] College of Information Engineering,Zhejiang University of Technology
[2] Zhejiang Provincial United Key Laboratory of Embedded Systems
[3] College of Science,Zhejiang University of Technology
基金
中国国家自然科学基金;
关键词
D O I
10.15918/j.jbit1004-0579.2022.072
中图分类号
TP309.7 [加密与解密];
学科分类号
081201 ; 0839 ; 1402 ;
摘要
The privacy-preserving problem for distributed fusion estimation scheme is concerned in this paper. When legitimate user wants to obtain consistent information from multiple sensors, it always employs a fusion center(FC) to gather local data and compute distributed fusion estimates(DFEs). Due to the existence of potential eavesdropper, the data exchanged among sensors, FC and user imperatively require privacy preservation. Hence, we propose a distributed confidentiality fusion structure against eavesdropper by using Paillier homomorphic encryption approach. In this case, FC cannot acquire real values of local state estimates, while it only helps calculate encrypted DFEs. Then, the legitimate user can successfully obtain the true values of DFEs according to the encrypted information and secret keys, which is based on the homomorphism of encryption. Finally,an illustrative example is provided to verify the effectiveness of the proposed methods.
引用
收藏
页码:551 / 558
页数:8
相关论文
共 50 条
  • [1] Distributed Privacy-Preserving Fusion Estimation Using Homomorphic Encryption
    Yan X.
    Zhuo S.
    Wu Y.
    Chen B.
    [J]. Journal of Beijing Institute of Technology (English Edition), 2022, 31 (06): : 551 - 558
  • [2] Privacy-Preserving Decentralized Optimization Using Homomorphic Encryption
    Huo, Xiang
    Liu, Mingxi
    [J]. IFAC PAPERSONLINE, 2020, 53 (05): : 630 - 633
  • [3] Privacy-Preserving Federated Learning Using Homomorphic Encryption
    Park, Jaehyoung
    Lim, Hyuk
    [J]. APPLIED SCIENCES-BASEL, 2022, 12 (02):
  • [4] Privacy-preserving Surveillance Methods using Homomorphic Encryption
    Bowditch, William
    Abramson, Will
    Buchanan, William J.
    Pitropakis, Nikolaos
    Hall, Adam J.
    [J]. ICISSP: PROCEEDINGS OF THE 6TH INTERNATIONAL CONFERENCE ON INFORMATION SYSTEMS SECURITY AND PRIVACY, 2020, : 240 - 248
  • [5] Privacy-Preserving Biometric Matching Using Homomorphic Encryption
    Pradel, Gaetan
    Mitchell, Chris
    [J]. 2021 IEEE 20TH INTERNATIONAL CONFERENCE ON TRUST, SECURITY AND PRIVACY IN COMPUTING AND COMMUNICATIONS (TRUSTCOM 2021), 2021, : 494 - 505
  • [6] Privacy-preserving set-based estimation using partially homomorphic encryption
    Alanwar, Amr
    Gassmann, Victor
    He, Xingkang
    Said, Hazem
    Sandberg, Henrik
    Johansson, Karl H.
    Althoff, Matthias
    [J]. EUROPEAN JOURNAL OF CONTROL, 2023, 71
  • [7] Privacy preserving distributed optimization using homomorphic encryption
    Lu, Yang
    Zhu, Minghui
    [J]. AUTOMATICA, 2018, 96 : 314 - 325
  • [8] On Homomorphic Encryption for Privacy-preserving Distributed Load Adaption in Smart Grids
    Brettschneider, Daniel
    Hoelker, Daniel
    Toenjes, Ralf
    Scheerhorn, Alfred
    [J]. 2016 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC), 2016,
  • [9] Privacy-Preserving Distributed Optimal Power Flow With Partially Homomorphic Encryption
    Wu, Tong
    Zhao, Changhong
    Zhang, Ying-Jun Angela
    [J]. IEEE TRANSACTIONS ON SMART GRID, 2021, 12 (05) : 4506 - 4521
  • [10] Privacy-Preserving Collective Learning With Homomorphic Encryption
    Paul, Jestine
    Annamalai, Meenatchi Sundaram Muthu Selva
    Ming, William
    Al Badawi, Ahmad
    Veeravalli, Bharadwaj
    Aung, Khin Mi Mi
    [J]. IEEE ACCESS, 2021, 9 : 132084 - 132096