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Safe consensus control of cooperative-competitive multi-agent systems via differential privacy

Jiayue Ma; Jiangping Hu

Kybernetika (2022)

  • Volume: 58, Issue: 3, page 426-439
  • ISSN: 0023-5954

Abstract

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This paper investigates a safe consensus problem for cooperative-competitive multi-agent systems using a differential privacy (DP) approach. Considering that the agents simultaneously interact cooperatively and competitively, we propose a novel DP bipartite consensus algorithm, which guarantees that the DP strategy only works on competitive pairs of agents. We then prove that the proposed algorithm can achieve the mean square bipartite consensus and ( p , r ) -accuracy. Furthermore, a differential privacy analysis is conducted, which shows that the performance of privacy protection is positively correlated with the number of neighbors. Thus, a practical method is established for the agents to select their own privacy levels. Finally, the simulation results are presented to demonstrate the validity of the proposed safe consensus algorithm.

How to cite

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Ma, Jiayue, and Hu, Jiangping. "Safe consensus control of cooperative-competitive multi-agent systems via differential privacy." Kybernetika 58.3 (2022): 426-439. <http://eudml.org/doc/298916>.

@article{Ma2022,
abstract = {This paper investigates a safe consensus problem for cooperative-competitive multi-agent systems using a differential privacy (DP) approach. Considering that the agents simultaneously interact cooperatively and competitively, we propose a novel DP bipartite consensus algorithm, which guarantees that the DP strategy only works on competitive pairs of agents. We then prove that the proposed algorithm can achieve the mean square bipartite consensus and $(p,r)$-accuracy. Furthermore, a differential privacy analysis is conducted, which shows that the performance of privacy protection is positively correlated with the number of neighbors. Thus, a practical method is established for the agents to select their own privacy levels. Finally, the simulation results are presented to demonstrate the validity of the proposed safe consensus algorithm.},
author = {Ma, Jiayue, Hu, Jiangping},
journal = {Kybernetika},
keywords = {differential privacy; safe consensus; cooperative-competitive multi-agent systems; Laplace distribution; $(p, r)$-accuracy},
language = {eng},
number = {3},
pages = {426-439},
publisher = {Institute of Information Theory and Automation AS CR},
title = {Safe consensus control of cooperative-competitive multi-agent systems via differential privacy},
url = {http://eudml.org/doc/298916},
volume = {58},
year = {2022},
}

TY - JOUR
AU - Ma, Jiayue
AU - Hu, Jiangping
TI - Safe consensus control of cooperative-competitive multi-agent systems via differential privacy
JO - Kybernetika
PY - 2022
PB - Institute of Information Theory and Automation AS CR
VL - 58
IS - 3
SP - 426
EP - 439
AB - This paper investigates a safe consensus problem for cooperative-competitive multi-agent systems using a differential privacy (DP) approach. Considering that the agents simultaneously interact cooperatively and competitively, we propose a novel DP bipartite consensus algorithm, which guarantees that the DP strategy only works on competitive pairs of agents. We then prove that the proposed algorithm can achieve the mean square bipartite consensus and $(p,r)$-accuracy. Furthermore, a differential privacy analysis is conducted, which shows that the performance of privacy protection is positively correlated with the number of neighbors. Thus, a practical method is established for the agents to select their own privacy levels. Finally, the simulation results are presented to demonstrate the validity of the proposed safe consensus algorithm.
LA - eng
KW - differential privacy; safe consensus; cooperative-competitive multi-agent systems; Laplace distribution; $(p, r)$-accuracy
UR - http://eudml.org/doc/298916
ER -

References

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  1. Altafini, C., , IEEE Trans. Automat. Control 58 (2013), 935-946. MR3038795DOI
  2. Cihan, O., , Kybernetika 56 (2020),578-597. MR4131744DOI
  3. Chen, B., Hu, J., Zhao, Y., Ghosh, B. K., , IEEE Trans. Syst. Man Cybernet. Syst. XX (2022), 1-12. DOI
  4. Chen, Z., Qin, J., .Li, B, Qi, H., Buchhorn, P., Shi, G., , Automatica 106 (2019), 374-383. MR3954046DOI
  5. Du, Y., Wang, Y., Zuo, Z., , IEEE Trans. Syst. Man Cybernet. Syst. 52 (2020), 1744-1754. DOI
  6. Dwork, C., Differential privacy: A survey of results., In: Proc. 5th International Conference on Theory and Applications of Models of Computation (2008), pp. 1-19. MR2472670
  7. Gao, L., Deng, S., Ren, W., Hu, C., , IEEE Trans. Cybernet. 51 (2021), 4075-4088. DOI
  8. He, J., Cai, L., Guan, X., , IEEE Trans. Signal Process. 68 (2020), 4069-4082. MR4128133DOI
  9. Hu, J., , Kybernetika 45 (2009), 768-784. Zbl1190.93003MR2599111DOI
  10. Hu, J., Wu, Y., , J. Franklin. Inst. 354 (2017), 4438-4456. MR3655777DOI
  11. Hu, J., Wu, Y., Li, T., Ghosh, B. K., , IEEE Trans. Automat. Control 64 (2019), 2122-2127. MR3951056DOI
  12. Huang, Z., Mitra, S., Dullerud, G., Differentially private iterative synchronous consensus., In: Proc. 2012 ACM Workshop on Privacy in the Electronic Society (2012) pp. 81-89. 
  13. Li, H., Li, X., , IEEE Trans. Circuits Syst. II Express Briefs 67 (2020), 1264-1268. DOI
  14. Li, P., Hu, J., Qiu, L., Zhao, Y., Ghosh, B. K., , IEEE Trans. Control Netw. Syst. 9 (2022), 356-366. MR4450544DOI
  15. Liu, X., Zhang, J., Wang, J., , Automatica 122 (2020), 109283. MR4161365DOI
  16. Ma, C., Xie, L., , IEEE Trans. Syst. Man Cybernet. Syst. 50 (2020), 1976-1981. DOI
  17. Nozari, E., Tallapragada, P., Cortes, J., , Automatica 81 (2017), 221-231. MR3654605DOI
  18. Peng, Z., Zhao, Y., Hu, J., Luo, R., Ghosh, B. K., Nguang, S. K., , IEEE Trans. Industr. Inform. 17 (2021), 7359-7367. DOI
  19. Rehák, B., Lynnyk, V., , Kybernetika 56 (2020), 363-381. MR4103722DOI
  20. Tang, Y., , Kybernetika 53 (2017), 282-295. MR3661353DOI
  21. Tang, Y., , IEEE Control Systems Lett. 5 (2021), 1201-1206. MR4211660DOI
  22. Tang, Y., Wang, X., , IEEE Trans. Automat. Control 66 (2021), 1733-1740. MR4240200DOI
  23. Wang, L., Liu, Y., Manchester, I., Shi, G., Differentially private distributed computation via public-private communication networks., arXiv preprint arXiv:2101.01376, 2021 MR4138618
  24. Wang, Y., Lam, J., Lin, H., , Neurocomputing 458 (2021), 87-98. DOI
  25. Wang, X., He, J., Cheng, P., Chen, J., , IEEE Trans. Netw. Sci. Engrg. 6 (2019), 928-939. MR4051631DOI
  26. Wu, Y., Zhao, Y., Hu, J., , IEEE Trans. Syst. Man Cybernet. Syst. 49 (2019), 2189-2199. DOI
  27. Zhang, Y., Hong, Y. Lou Y., Xie, L., , IEEE Trans. Wirel. Commun. 14 (2015), 3131-3142. DOI
  28. Zuo, Z., Tian, R., Han, Q., Wang, Y., Zhang, W., , Neurocomputing 468 (2022), 11-21. DOI

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