Resilient consensus for multi-agent systems subject to differential privacy requirements
We consider multi-agent systems interacting over directed network topologies where a subset of agents is adversary/faulty and where the non-faulty agents have the goal of reaching consensus, while fulfilling a differential privacy requirement on their initial conditions. To address this problem, we...
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| Published in | Automatica (Oxford) Vol. 106; pp. 18 - 26 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.08.2019
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| Subjects | |
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| Abstract | We consider multi-agent systems interacting over directed network topologies where a subset of agents is adversary/faulty and where the non-faulty agents have the goal of reaching consensus, while fulfilling a differential privacy requirement on their initial conditions. To address this problem, we develop an update law for the non-faulty agents. Specifically, we propose a modification of the so-called Mean-Subsequence-Reduced (MSR) algorithm, the Differentially Private MSR (DP-MSR) algorithm, and characterize three important properties of the algorithm: correctness, accuracy and differential privacy. We show that if the network topology is (2f+1)-robust, then the algorithm allows the non-faulty agents to reach consensus despite the presence of up to f faulty agents and we characterize the accuracy of the algorithm. Furthermore, we also show in two important cases that our distributed algorithm can be tuned to guarantee differential privacy of the initial conditions and the differential privacy requirement is related to the maximum network degree. The results are illustrated via simulations. |
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| AbstractList | We consider multi-agent systems interacting over directed network topologies where a subset of agents is adversary/faulty and where the non-faulty agents have the goal of reaching consensus, while fulfilling a differential privacy requirement on their initial conditions. To address this problem, we develop an update law for the non-faulty agents. Specifically, we propose a modification of the so-called Mean-Subsequence-Reduced (MSR) algorithm, the Differentially Private MSR (DP-MSR) algorithm, and characterize three important properties of the algorithm: correctness, accuracy and differential privacy. We show that if the network topology is (2f+1)-robust, then the algorithm allows the non-faulty agents to reach consensus despite the presence of up to f faulty agents and we characterize the accuracy of the algorithm. Furthermore, we also show in two important cases that our distributed algorithm can be tuned to guarantee differential privacy of the initial conditions and the differential privacy requirement is related to the maximum network degree. The results are illustrated via simulations. |
| Author | Russo, Giovanni Fiore, Davide |
| Author_xml | – sequence: 1 givenname: Davide surname: Fiore fullname: Fiore, Davide email: davide.fiore@unina.it organization: Department of Electrical Engineering and Information Technology, University of Naples Federico II, Via Claudio 21, 80125 Naples, Italy – sequence: 2 givenname: Giovanni surname: Russo fullname: Russo, Giovanni email: giovanni.russo1@ucd.ie organization: School of Electrical and Electronic Engineering, University College Dublin, Belfield, Dublin 4, Ireland |
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| Keywords | Differential privacy Networked control systems Resilient consensus Multi-agent systems |
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| SubjectTerms | Differential privacy Multi-agent systems Networked control systems Resilient consensus |
| Title | Resilient consensus for multi-agent systems subject to differential privacy requirements |
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