Cloud-Enabled Differentially Private Multiagent Optimization With Constraints
We present an optimization framework for solving multiagent convex programs subject to inequality constraints while keeping the agents' state trajectories private. Each agent has an objective function depending only upon its own state and the agents are collectively subject to global constraint...
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| Vydáno v: | IEEE transactions on control of network systems Ročník 5; číslo 4; s. 1693 - 1706 |
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| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Piscataway
IEEE
01.12.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 2325-5870, 2372-2533 |
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| Abstract | We present an optimization framework for solving multiagent convex programs subject to inequality constraints while keeping the agents' state trajectories private. Each agent has an objective function depending only upon its own state and the agents are collectively subject to global constraints. The agents do not directly communicate with each other but instead route messages through a trusted cloud computer. The cloud adds noise to data being sent to the agents in accordance with the framework of differential privacy and, thus, keeps each agent's state trajectory private from all other agents and any eavesdroppers. This private problem can be viewed as a stochastic variational inequality, and it is solved using a projection-based method for solving variational inequalities that resemble a noisy primal-dual gradient algorithm. Convergence of the optimization algorithm in the presence of noise is proven, and a quantifiable tradeoff between privacy and convergence is extracted from this proof. Simulation results are provided that demonstrate numerical convergence for both <inline-formula><tex-math notation="LaTeX">\epsilon</tex-math></inline-formula>-differential privacy and <inline-formula><tex-math notation="LaTeX">(\epsilon, \delta)</tex-math></inline-formula>-differential privacy. |
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| AbstractList | We present an optimization framework for solving multiagent convex programs subject to inequality constraints while keeping the agents’ state trajectories private. Each agent has an objective function depending only upon its own state and the agents are collectively subject to global constraints. The agents do not directly communicate with each other but instead route messages through a trusted cloud computer. The cloud adds noise to data being sent to the agents in accordance with the framework of differential privacy and, thus, keeps each agent's state trajectory private from all other agents and any eavesdroppers. This private problem can be viewed as a stochastic variational inequality, and it is solved using a projection-based method for solving variational inequalities that resemble a noisy primal-dual gradient algorithm. Convergence of the optimization algorithm in the presence of noise is proven, and a quantifiable tradeoff between privacy and convergence is extracted from this proof. Simulation results are provided that demonstrate numerical convergence for both [Formula Omitted]-differential privacy and [Formula Omitted]-differential privacy. We present an optimization framework for solving multiagent convex programs subject to inequality constraints while keeping the agents' state trajectories private. Each agent has an objective function depending only upon its own state and the agents are collectively subject to global constraints. The agents do not directly communicate with each other but instead route messages through a trusted cloud computer. The cloud adds noise to data being sent to the agents in accordance with the framework of differential privacy and, thus, keeps each agent's state trajectory private from all other agents and any eavesdroppers. This private problem can be viewed as a stochastic variational inequality, and it is solved using a projection-based method for solving variational inequalities that resemble a noisy primal-dual gradient algorithm. Convergence of the optimization algorithm in the presence of noise is proven, and a quantifiable tradeoff between privacy and convergence is extracted from this proof. Simulation results are provided that demonstrate numerical convergence for both <inline-formula><tex-math notation="LaTeX">\epsilon</tex-math></inline-formula>-differential privacy and <inline-formula><tex-math notation="LaTeX">(\epsilon, \delta)</tex-math></inline-formula>-differential privacy. |
| Author | Hale, Matthew T. Egerstedt, Magnus |
| Author_xml | – sequence: 1 givenname: Matthew T. orcidid: 0000-0003-3991-1680 surname: Hale fullname: Hale, Matthew T. email: matthewhale@ufl.edu organization: Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL, USA – sequence: 2 givenname: Magnus surname: Egerstedt fullname: Egerstedt, Magnus email: magnus@gatech.edu organization: School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA |
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| SubjectTerms | Algorithms Cloud computing Computer simulation Convergence Data privacy Decentralized control Eavesdropping Linear programming Multiagent systems Networked control systems Optimization Privacy Trajectories Trajectory |
| Title | Cloud-Enabled Differentially Private Multiagent Optimization With Constraints |
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