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Dynamics-Based Algorithm-Level Privacy Preservation for Push-Sum Average Consensus (2304.08018v3)

Published 17 Apr 2023 in cs.MA

Abstract: In the intricate dance of multi-agent systems, achieving average consensus is not just vital--it is the backbone of their functionality. In conventional average consensus algorithms, all agents reach an agreement by individual calculations and sharing information with their respective neighbors. Nevertheless, the information interactions that occur in the communication network may make sensitive information be revealed. In this paper, we develop a new privacy-preserving average consensus method on unbalanced directed networks. Specifically, we ensure privacy preservation by carefully embedding randomness in mixing weights to confuse communications and introducing an extra auxiliary parameter to mask the state-updated rule in several initial iterations. In parallel, we exploit the intrinsic robustness of consensus dynamics to guarantee that the average consensus is precisely achieved. Theoretical results demonstrate that the designed algorithms can converge linearly to the exact average consensus value and can guarantee privacy preservation of agents against both honest-but-curious and eavesdropping attacks. The designed algorithms are fundamentally different compared to differential privacy based algorithms that enable privacy preservation via sacrificing consensus performance. Finally, numerical experiments validate the correctness of the theoretical findings.

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References (45)
  1. F. Acciani, P. Frasca, G. Heijenk, and A. A. Stoorvogel, “Achieving robust average consensus over lossy wireless networks”, IEEE Transactions on Control of Network Systems, vol. 6, no. 1, pp. 127–137, 2019.
  2. F. C. Souza, S. R. B. Dos Santos, A. M. de Oliveira, and S. N. Givigi, “Influence of network topology on UAVs formation control based on distributed consensus,” in IEEE International Systems Conference, 2022, pp. 1–8.
  3. B. Du, R. Mao, N. Kong, D. Sun, “Distributed data fusion for on-scene signal sensing with a multi-UAV system,” in IEEE Transactions on Control of Network Systems, vol. 7, no. 3, pp. 1330–1341, 2020.
  4. J. N. Tsitsiklis, “Problems in decentralized decision making and computation,” Ph.D. dissertation, 1984.
  5. Z. Zhang and M. Y. Chow, “Incremental cost consensus algorithm in a smart grid environment,” in IEEE Power and Energy Society General Meeting, 2011, pp. 1–6.
  6. C. Dwork, F. McSherry, K. Nissim, and A. Smith, “Calibrating noise to sensitivity in private data analysis,” in Proceedings of the 3rd Theory Cryptography Conference, 2006, pp. 265–284.
  7. Z. Huang, S. Mitra, and N. Vaidya, “Differentially private distributed optimization,” in International Conference on Distributed Computing and Networking, 2015, pp. 1–10.
  8. E. Nozari, P. Tallapragada, and J. Cortés, “Differentially private average consensus: Obstructions, trade-offs, and optimal algorithm design,” Automatica, vol. 81, pp. 221–231, 2017.
  9. Z. Huang, S. Mitra, and G. Dullerud, “Differentially private iterative synchronous consensus,” in Proceedings of the 2012 ACM Workshop on Privacy in the Electronic Society, 2012, pp. 81–90.
  10. L. Gao, S. Deng, W. Ren, and C. Hu, “Differentially private consensus with quantized communication,” IEEE Transactions on Cybernetics, vol. 51, no. 8, pp. 4075–4088, Aug. 2021.
  11. D. Ye, T. Zhu, W. Zhou, and S. Y. Philip, “Differentially private malicious agent avoidance in multiagent advising learning,” IEEE Transactions on Cybernetics, vol. 50, no. 10, pp. 4214–4227, Oct. 2020.
  12. M. Kefayati, M. S. Talebi, B. H. Khalaj, and H. R. Rabiee, “Secure consensus averaging in sensor networks using random offsets,” in IEEE International Conference on Telecommunications and Malaysia International Conference on Communications, 2007, pp. 556–560.
  13. Y. Mo and R. M. Murray, “Privacy preserving average consensus,” IEEE Transactions on Automatic Control, vol. 62, no. 2, pp. 753–765, 2017.
  14. N. E. Manitara and C. N. Hadjicostis, “Privacy-preserving asymptotic average consensus,” in European Control Conference (ECC), 2013, pp. 760–765.
  15. S. Pequito, S. Kar, S. Sundaram, and A. P. Aguiar, “Design of communication networks for distributed computation with privacy guarantees,” in Proceedings of the 53rd IEEE Conference on Decision and Control, 2014, pp. 1370–1376.
  16. I. D. Ridgley, R. A. Freeman, and K. M. Lynch, “Simple, private, and accurate distributed averaging,” in Proceedings of IEEE 57th Annual Allerton Conference on Communication, Control, and Computing, 2019, pp. 446–452.
  17. A. Alaeddini, K. Morgansen, and M. Mesbahi, “Adaptive communication networks with privacy guarantees,” in Proceedings of American Control Conference, 2017, pp. 4460–4465.
  18. M. Kishida, “Encrypted average consensus with quantized control law,” in Proceedings of IEEE Conference on Decision and Control, 2018, pp. 5850–5856.
  19. C. N. Hadjicostis and A. D. Dominguez-Garcia, “Privacy-preserving distributed averaging via homomorphically encrypted ratio consensus,” IEEE Transactions on Automatic Control, vol. 65, no. 9, pp. 3887–3894, Sep. 2020.
  20. W. Fang, M. Zamani, and Z. Chen, “Secure and privacy preserving consensus for second-order systems based on paillier encryption,” Systems & Control Letters, vol. 148, pp. 104869, 2021.
  21. M. Ruan, H. Gao, and Y. Wang, “Secure and privacy-preserving consensus,” IEEE Transactions on Automatic Control, vol. 64, no. 10, pp. 4035–4049, Oct. 2019.
  22. Y. Wang, “Privacy-preserving average consensus via state decomposition,” IEEE Transactions on Automatic Control, vol. 64, no. 11, pp. 4711–4716, 2019.
  23. X. Chen, L. Huang, K. Ding, S. Dey, and L. Shi, “Privacy-preserving push-sum average consensus via state decomposition,” IEEE Transactions on Automatic Control, doi:10.1109/TAC.2023.3256479, 2023.
  24. H. Gao, C. Zhang, M. Ahmad, and Y. Q. Wang, “Privacy-preserving average consensus on directed graphs using push-sum,” in IEEE Conference on Communications and Network Security (CNS), 2018, pp. 1–9.
  25. H. Gao, and Y. Wang, “Algorithm-level confidentiality for average consensus on time-varying directed graphs,” IEEE Transactions on Network Science and Engineering, vol. 9, no. 2, pp. 918–931, 2022.
  26. Y. Liu, H. Gao, J. Du, and Y. Zhi, “Dynamics based privacy preservation for average consensus on directed graphs,” in Proceedings of the 41st Chinese Control Conference, 2022, pp. 4955–4961.
  27. C. Fioravanti, V. Bonagura, G. Oliva, C. N. Hadjicostis, and S. Panzieri, “Exploiting the synchronization of nonlinear dynamics to secure distributed consensus,” IEEE Open J. Control Syst., vol. 2, pp. 249–262, 2023.
  28. S. Gade and N. H. Vaidya, “Private optimization on networks,” In 2018 Annual American Control Conference (ACC), pp. 1402–1409, 2018.
  29. D. Han, K. Liu, H. Sandberg, S. Chai, and Y. Xia, “Privacy-preserving dual averaging with arbitrary initial conditions for distributed optimization,” IEEE Transactions on Automatic Control, vol. 67, no. 6, pp. 3172–3179, Jun. 2022.
  30. H. Gao, Y. Wang, and A. Nedić, “Dynamics based privacy preservation in decentralized optimization,” Automatica, vol. 151, pp. 110878, 2023.
  31. H. Cheng, X. Liao, H. Li, and Y. Zhao, “Privacy-preserving push-pull method for decentralized optimization via state decomposition,” arXiv preprint arXiv: 2308.08164, 2023.
  32. Y. Lin, K. Liu, D. Han, and Y. Xia, “Statistical privacy-preserving online distributed nash equilibrium tracking in aggregative games,” IEEE Transactions on Automatic Control, vol. 69, no. 1, pp. 323–330, Jan. 2024.
  33. H. Cheng, X. Liao, and H. Li, “Distributed online private learning of convex nondecomposable objectives,” IEEE Transactions on Network Science and Engineering, vol. 11, no. 2, pp. 1716–1728, Mar.- Apr. 2024.
  34. D. Kempe, A. Dobra, and J. Gehrke, “Gossip-based computation of aggregate information,” in Proceedings of 44th Annual IEEE Symposium on Foundations of Computer Science, 2003, pp. 482–491.
  35. F. Bénézit, V. Blondel, P. Thiran, J. Tsitsiklis, and M. Vetterli, “Weighted gossip: Distributed averaging using non-doubly stochastic matrices,” in Proceedings of 2010 IEEE International Symposium on Information Theory, 2010, pp. 1753–1757.
  36. C. N. Hadjicostis, A. D. Domínguez-García, and T. Charalambous, “Distributed averaging and balancing in network systems: With applications to coordination and control,” Foundations and Trends in Systems and Control, vol. 5, no. 2–3, pp. 99–292, 2018.
  37. K. Liu, H. Kargupta, and J. Ryan, “Random projection-based multiplicative data perturbation for privacy preserving distributed data mining,” IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 1, pp. 92–106, 2005.
  38. S. Han, W. K. Ng, L. Wan, and V. C. Lee, “Privacy-preserving gradient-descent methods,” IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 6, pp. 884–899, 2009.
  39. N. Cao, C. Wang, M. Li, K. Ren, and W. Lou, “Privacy-preserving multi-keyword ranked search over encrypted cloud data,” IEEE Transactions on Parallel and Distributed Systems, vol. 25, no. 1, pp. 222–233, 2013.
  40. E. Seneta, “Non-negative matrices and markov chains,” Springer, 1973.
  41. J. A. Fill, “Eigenvalue bounds on convergence to stationarity for nonreversible markov chains with an application to the exclusion process,” The annals of applied probability, 1991, pp. 62–87.
  42. R. A. Horn and C. R. Johnson, “Matrix Analysis,” Cambridge university press Press, 2012.
  43. Y. Lu and M. Zhu, “On privacy preserving data release of linear dynamic networks,” Automatica, vol. 115, pp. 108839, 2020.
  44. A. Machanavajjhala, D. Kifer, J. Gehrke, and M. Venkitasubramaniam, “L𝐿Litalic_L-diversity: Privacy beyond k𝑘kitalic_k-anonymity,” ACM Transactions on Knowledge Discovery from Data, vol. 1, no. 1, pp. 3–es, 2007.
  45. A. Nedić, A. Ozdaglar, and P. A. Parrilo, “Constrained consensus and optimization in multi-agent networks,” IEEE Transactions on Automatic Control, vol. 55, no. 4, pp. 922–938, 2010.
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