Concentrated differentially private average consensus algorithm for a discrete-time network with heterogeneous dynamics |
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| Institution: | 1. School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China;2. Key Laboratory of Image Processing and Intelligent Control, Ministry of Education, Huazhong University of Science and Technology, Wuhan 430074, China;1. Department of Mathematics, Shanghai University, Shanghai 200444, China;2. School of Mathematics and Computing Science, Guangxi Key Laboratory of Cryptography and Information Security, Guilin University of Electronic Technology, Guilin 541004, China;1. School of Computer Science and Technology, Huaiyin Normal University, Huaian 223300, Jiangsu, China;2. School of Mathematics, Southeast University, Nanjing 210096, China;3. Yonsei Frontier Lab, Yonsei University, Seoul 03722, South Korea;4. School of Mathematical Science, Huaiyin Normal University, Huaian 223300, Jiangsu, China |
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| Abstract: | This paper presents a privacy-preserving average consensus algorithm for a discrete-time network with heterogeneous dynamic nodes in the presence of Gaussian privacy noises. Rényi divergence is used to measure the privacy, and a distributed algorithm is proposed for each node in the network to protect the initial output state and ensure consensus almost surely. The convergence rate of the proposed algorithm relates to the communication topology, dynamics of systems, and decaying rates of privacy noises. Moreover, by increasing neighbors of nodes in the network, the proposed algorithm can strengthen preservation. To demonstrate the theoretical results, a numerical example is carried out on a network of one hundred nodes. |
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