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Big Data Mining and Analytics 2024, 7(1): 131-141 https://doi.org/10.26599/BDMA.2023.9020008
Open Access | Issue | Published: 25 December 2023

Multi-Smart Meter Data Encryption Scheme Basedon Distributed Differential Privacy

Show Author's Information Renwu Yan1( ),  Yang Zheng1,  Ning Yu2,  Cen Liang1
School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou 350118, China
Department of Computing Sciences at The College at Brockport, State University of New York, New York, NY 14420, USA
Keywords:
smart grid, data aggregation, cloud computing, differential privacy, homomorphic encryption
Cite this article:
Yan R, Zheng Y, Yu N, et al. Multi-Smart Meter Data Encryption Scheme Basedon Distributed Differential Privacy. Big Data Mining and Analytics, 2024, 7(1): 131-141. https://doi.org/10.26599/BDMA.2023.9020008
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Abstract Full text About this article

Under the general trend of the rapid development of smart grids, data security and privacy are facing serious challenges; protecting the privacy data of single users under the premise of obtaining user-aggregated data has attracted widespread attention. In this study, we propose an encryption scheme on the basis of differential privacy for the problem of user privacy leakage when aggregating data from multiple smart meters. First, we use an improved homomorphic encryption method to realize the encryption aggregation of users’ data. Second, we propose a double-blind noise addition protocol to generate distributed noise through interaction between users and a cloud platform to prevent semi-honest participants from stealing data by colluding with one another. Finally, the simulation results show that the proposed scheme can encrypt the transmission of multi-intelligent meter data under the premise of satisfying the differential privacy mechanism. Even if an attacker has enough background knowledge, the security of the electricity information of one another can be ensured.


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About this article

Multi-Smart Meter Data Encryption Scheme Basedon Distributed Differential Privacy

Show Author's information Renwu Yan1( ), Yang Zheng1, Ning Yu2, Cen Liang1
School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou 350118, China
Department of Computing Sciences at The College at Brockport, State University of New York, New York, NY 14420, USA

Abstract

Under the general trend of the rapid development of smart grids, data security and privacy are facing serious challenges; protecting the privacy data of single users under the premise of obtaining user-aggregated data has attracted widespread attention. In this study, we propose an encryption scheme on the basis of differential privacy for the problem of user privacy leakage when aggregating data from multiple smart meters. First, we use an improved homomorphic encryption method to realize the encryption aggregation of users’ data. Second, we propose a double-blind noise addition protocol to generate distributed noise through interaction between users and a cloud platform to prevent semi-honest participants from stealing data by colluding with one another. Finally, the simulation results show that the proposed scheme can encrypt the transmission of multi-intelligent meter data under the premise of satisfying the differential privacy mechanism. Even if an attacker has enough background knowledge, the security of the electricity information of one another can be ensured.

Keywords: smart grid, data aggregation, cloud computing, differential privacy, homomorphic encryption

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Publication history

Received: 31 October 2022
Revised: 25 April 2023
Accepted: 26 April 2023
Published: 25 December 2023
Issue date: March 2024

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© The author(s) 2023.

Acknowledgements

Acknowledgment

This work was supported by the National Natural Science Foundation of China (No. 51677059) and the Fujian Provincial University Engineering Research Center Open Fund (No. KF-D21009).

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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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