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Privacy-Enhanced Mean-Variance Scheme Against Malicious Signature Attacks in Smart Grids

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Ubiquitous Security (UbiSec 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1557))

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Abstract

Secure multidimensional data aggregation (SMDA) has been widely investigated to meet the requirement of protecting individual users’ real-time electricity consumption data privacy in smart grid. However, most previous proposals require decryption of encrypted data to obtain the mean and variance, which leads to the inefficient and insecure in the protocols. This article presents an efficient and privacy-enhanced mean-variance scheme (namely PMVS) to provide the privacy-preserving, in which the Paillier cryptosystem is adopted in a fog-based architecture. To achieve efficient authentication functionality, batch verification technology is applied in our scheme. Our scheme provides two new features: Firstly, Electricity Service Provider (ESP) can directly obtain the mean and variance by decrypting the received ciphertext. Secondly, the PMVS can also resist malicious signature attacks. By identifying invalid signatures, the verified signatures can be aggregated, effectively preventing malicious signature attacks from causing batch verification to fail all the time and failing to enter the secure computing stage. The security analysis shows that the proposed scheme is secure and can preserve the meters’ privacy.

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Acknowledgements

This work is partially supported by the National Natural Science Foundation of China under grants 62072134 and U2001205, and the Key projects of Guangxi Natural Science Foundation under grant 2019JJD170020.

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Authors and Affiliations

  1. Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China

    Yuzhu Wang, Peiheng Zhang, Haitao Zhan & Mingwu Zhang

Authors
  1. Yuzhu Wang
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  2. Peiheng Zhang
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  3. Haitao Zhan
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  4. Mingwu Zhang
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Editor information

Editors and Affiliations

  1. Guangzhou University, Guangzhou, China

    Guojun Wang

  2. University of Texas at San Antonio, San Antonio, TX, USA

    Kim-Kwang Raymond Choo

  3. University of Queensland, Brisbane, QLD, Australia

    Ryan K. L. Ko

  4. Hunan University, Changsha, China

    Yang Xu

  5. University of Trento, Trento, Italy

    Bruno Crispo

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Wang, Y., Zhang, P., Zhan, H., Zhang, M. (2022). Privacy-Enhanced Mean-Variance Scheme Against Malicious Signature Attacks in Smart Grids. In: Wang, G., Choo, KK.R., Ko, R.K.L., Xu, Y., Crispo, B. (eds) Ubiquitous Security. UbiSec 2021. Communications in Computer and Information Science, vol 1557. Springer, Singapore. https://doi.org/10.1007/978-981-19-0468-4_11

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  • DOI: https://doi.org/10.1007/978-981-19-0468-4_11

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-0467-7

  • Online ISBN: 978-981-19-0468-4

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