A Privacy Preserving Distributed Optimization Algorithm for Economic Dispatch Over Time-Varying Directed Networks

Mao, Shuai; Tang, Yang; Dong, Ziwei; Meng, Ke; Zhang, Dong; Qian, Feng

doi:10.1109/tii.2020.2996198

Cited by 74 publications

(11 citation statements)

References 37 publications

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“…Besides, c in (5a) and d in (5b) could be private information held by either the agents or the SO depending on particular applications, leading to different situations in privacy preservation. This paper develops a privacy-preserving paradigm that protects the privacy of all the participants in solving problem (1) and enjoys the scalability of the decentralized optimization.…”

Section: A Problem Formulationmentioning

confidence: 99%

“…Large-scale optimization problems broadly exist in industrial cyber-physical system (ICPS) applications, e.g., economic dispatch in power system [1], traffic congestion control in transportation system [2], distributed energy resource control in smart grid [3], and resource allocation [4]. Owing to the scalability and flexibility, decentralized optimization has been extensively adopted to speed up large-scale optimization.…”

Section: Introductionmentioning

confidence: 99%

“…The contribution of this paper is four-fold: (1) We synthesize cryptography and decentralized optimization to develop a novel privacy-preserving scalable optimization paradigm for strongly coupled optimization problems. (2) An idea of coefficient assignment is proposed to preserve the general privacy of both the agents and the SO.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Cryptography-Based Privacy-Preserving Decentralized Optimization Paradigm

Huo

Liu

2020

Preprint

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Existing large-scale optimization schemes are challenged by both scalability and cyber-security. With the favorable scalability, adaptability, and flexibility, decentralized and distributed optimization paradigms are widely adopted in cyberphysical system applications. However, most existing approaches heavily rely on explicit information exchange between agents or between agents and the system operator, leading the entire framework prone to privacy risks. To tackle this issue, this paper synthesizes cryptography and decentralized optimization techniques to develop a novel privacy-preserving decentralized optimization paradigm. The proposed paradigm is generically applicable to strongly coupled convex optimization problems with nonseparable objective functions and linearly coupled constraints. The security and accuracy of the proposed paradigm are verified via numerical examples.

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Section: A Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Novel Cryptography-Based Privacy-Preserving Decentralized Optimization Paradigm

Huo

Liu

2020

Preprint

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“…In practice, the information flows among sensors may not be bidirectional due to the different communication ranges, e.g., the coordinated vechicle control problem [16] and the economic dispatch problem [17]. To address privacy leakage in distributed optimization for agents interacting over an unbalanced graphs, Mao et al [18] designed a privacy-preserving algorithm based on the push-gradient method with a decaying stepsize, which is implemented via a case study to the economic dispatch problem. Nevertheless, the algorithm in [18] lacked a formal privacy notion and it cannot achieve differential privacy.…”

Section: Introductionmentioning

confidence: 99%

“…To address privacy leakage in distributed optimization for agents interacting over an unbalanced graphs, Mao et al [18] designed a privacy-preserving algorithm based on the push-gradient method with a decaying stepsize, which is implemented via a case study to the economic dispatch problem. Nevertheless, the algorithm in [18] lacked a formal privacy notion and it cannot achieve differential privacy.…”

Section: Introductionmentioning

confidence: 99%

A Differential Private Method for Distributed Optimization in Directed Networks via State Decomposition

Chen¹,

Huang²,

He³

et al. 2021

Preprint

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In this paper, we study the problem of consensusbased distributed optimization where a network of agents, abstracted as a directed graph, aims to minimize the sum of all agents' cost functions collaboratively. In existing distributed optimization approaches (Push-Pull/AB) for directed graphs, all agents exchange their states with neighbors to achieve the optimal solution with a constant stepsize, which may lead to the disclosure of sensitive and private information. For privacy preservation, we propose a novel state-decomposition based gradient tracking approach (SD-Push-Pull) for distributed optimzation over directed networks that preserves differential privacy, which is a strong notion that protects agents' privacy against an adversary with arbitrary auxiliary information. The main idea of the proposed approach is to decompose the gradient state of each agent into two sub-states. Only one substate is exchanged by the agent with its neighbours over time, and the other one is kept private. That is to say, only one substate is visible to an adversary, protecting the privacy from being leaked. It is proved that under certain decomposition principles, a bound for the sub-optimality of the proposed algorithm can be derived and the differential privacy is achieved simultaneously. Moreover, the trade-off between differential privacy and the optimization accuracy is also characterized. Finally, a numerical simulation is provided to illustrate the effectiveness of the proposed approach.

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Privacy‐preserving distributed projected one‐point bandit online optimization over directed graphs

Wei

Yang

et al. 2023

Asian Journal of Control

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The distributed online optimization (DOO) problem with privacy‐preserving properties over multiple agents is considered in this paper, where the network model is built by a strongly connected directed graph. To solve this problem, a stochastic bandit DOO algorithm based on differential privacy is proposed. This algorithm uses row‐ and column‐stochastic matrix as the weighting matrices, the requirement of the double random weighting matrix is released. To handle the unknown objective function, the one‐point bandit is used to estimate the true gradient information, and the estimated gradient information is used to update of decision variables. Different from the existing DOO algorithms that ignore privacy issues, this algorithm successfully protects the privacy of nodes through a differential privacy policy. Theoretical results show that the algorithm can not only achieve sublinear regret bounds but also protect the privacy of nodes. Finally, simulation results verify the effectiveness of the algorithm.

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A Privacy Preserving Distributed Optimization Algorithm for Economic Dispatch Over Time-Varying Directed Networks

Cited by 74 publications

References 37 publications

A Novel Cryptography-Based Privacy-Preserving Decentralized Optimization Paradigm

A Novel Cryptography-Based Privacy-Preserving Decentralized Optimization Paradigm

A Differential Private Method for Distributed Optimization in Directed Networks via State Decomposition

Privacy‐preserving distributed projected one‐point bandit online optimization over directed graphs

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