Cooperative secure parameter identification of multi-participant ARX systems — a threshold Paillier cryptosystem-based least-squares identification algorithm

被引:0
|
作者
Tan J. [1 ,2 ]
Wang J. [3 ]
Zhang J. [1 ,2 ]
机构
[1] Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing
[2] School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing
[3] School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing
关键词
least squares method; multi-participant ARX system; privacy security; system identification; threshold Paillier cryptosystem;
D O I
10.1360/SSI-2023-0140
中图分类号
学科分类号
摘要
In this paper, the cooperative secure parameter identification problem of stochastic linear systems with multiple participants is studied, and a threshold Paillier cryptosystem-based secure multiparty least-squares identification algorithm is proposed. Specifically, by encoding positive and negative integers properly, the encryption object and homomorphic properties of the (threshold) Paillier cryptosystem are extended from nonnegative integers to integers. Using the threshold Paillier cryptosystem and the method for data segmentation along the time axis, the corresponding secure multiparty parameter identification algorithm is designed. The condition of plaintext space size required for correct encryption and decryption, the condition of the time slicing length to ensure privacy security, and the quantitative relationship between estimation error and encryption quantization error under certain conditions are given. We prove that as long as an appropriate length for time slicing is chosen, the specific private information of any given participant still cannot be obtained, even if all other participants colluded. Finally, the efficiency of the algorithm is verified using a numerical example. © 2023 Chinese Institute of Electronics. All rights reserved.
引用
收藏
页码:2472 / 2492
页数:20
相关论文
共 26 条
  • [21] Moore J B., On strong consistency of least squares identification algorithms, Automatica, 14, pp. 505-509, (1978)
  • [22] Lai T L, Wei C Z., Least squares estimates in stochastic regression models with applications to identification and control of dynamic systems, Annals Statistics, 10, pp. 154-166, (1982)
  • [23] Chen H F, Guo L., Convergence rate of least-squares identification and adaptive control for stochastic systems, Int J Control, 44, pp. 1459-1476, (1986)
  • [24] Guo L, Chen H F., The åström-Wittenmark self-tuning regulator revisited and ELS-based adaptive trackers, IEEE Trans Automat Contr, 36, pp. 802-812, (1991)
  • [25] Chen H F, Zhao W., Recursive Identification and Parameter Estimation, (2014)
  • [26] Bryant R E, O'Hallaron D R., Computer Systems: A Programmer’s Perspective, (2016)