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iEnergy 2022, 1(3): 351-362 https://doi.org/10.23919/IEN.2022.0045
Article | Open Access | Issue | Published: 20 September 2022

Fully privacy-preserving distributed optimization in power systems based on secret sharing

Show Author's Information Nianfeng Tian,  Qinglai Guo( ),  Hongbin Sun,  Xin Zhou
The State Key Laboratory of Power Systems, Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
Keywords:
secret sharing, privacy preservation, distributed optimization, Secure multi-party computation
Cite this article:
Tian N, Guo Q, Sun H, et al. Fully privacy-preserving distributed optimization in power systems based on secret sharing. iEnergy, 2022, 1(3): 351-362. https://doi.org/10.23919/IEN.2022.0045
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Abstract Full text About this article

With the increasing development of smart grid, multi-party cooperative computation between several entities has become a typical characteristic of modern energy systems. Traditionally, data exchange among parties is inevitable, rendering how to complete multi-party collaborative optimization without exposing any private information a critical issue. This paper proposes a fully privacy-preserving distributed optimization framework based on secure multi-party computation (SMPC) with secret sharing protocols. The framework decomposes the collaborative optimization problem into a master problem and several subproblems. The process of solving the master problem is executed in the SMPC framework via the secret sharing protocols among agents. The relationships of agents are completely equal, and there is no privileged agent or any third party. The process of solving subproblems is conducted by agents individually. Compared to the traditional distributed optimization framework, the proposed SMPC-based framework can fully preserve individual private information. Exchanged data among agents are encrypted and no private information disclosure is assured. Furthermore, the framework maintains a limited and acceptable increase in computational costs while guaranteeing optimality. Case studies are conducted on test systems of different scales to demonstrate the principle of secret sharing and verify the feasibility and scalability of the proposed methodology.


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About this article

Fully privacy-preserving distributed optimization in power systems based on secret sharing

Show Author's information Nianfeng Tian, Qinglai Guo( ), Hongbin Sun, Xin Zhou
The State Key Laboratory of Power Systems, Department of Electrical Engineering, Tsinghua University, Beijing 100084, China

Abstract

With the increasing development of smart grid, multi-party cooperative computation between several entities has become a typical characteristic of modern energy systems. Traditionally, data exchange among parties is inevitable, rendering how to complete multi-party collaborative optimization without exposing any private information a critical issue. This paper proposes a fully privacy-preserving distributed optimization framework based on secure multi-party computation (SMPC) with secret sharing protocols. The framework decomposes the collaborative optimization problem into a master problem and several subproblems. The process of solving the master problem is executed in the SMPC framework via the secret sharing protocols among agents. The relationships of agents are completely equal, and there is no privileged agent or any third party. The process of solving subproblems is conducted by agents individually. Compared to the traditional distributed optimization framework, the proposed SMPC-based framework can fully preserve individual private information. Exchanged data among agents are encrypted and no private information disclosure is assured. Furthermore, the framework maintains a limited and acceptable increase in computational costs while guaranteeing optimality. Case studies are conducted on test systems of different scales to demonstrate the principle of secret sharing and verify the feasibility and scalability of the proposed methodology.

Keywords: secret sharing, privacy preservation, distributed optimization, Secure multi-party computation

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Received: 14 August 2022
Accepted: 15 October 2022
Published: 20 September 2022
Issue date: September 2022

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Copyright: by the author(s). The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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