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PDAP-GAN: Generative Adversarial Network for Power Data Anonymization Protection

Published: 31 July 2024 Publication History

Abstract

Aiming at the problem that in the current environment of power data usage, the increased openness, frequent flow and complex interaction objects of data lead to the prevalence of data leakage risk throughout the data life cycle, this study proposes a generative adversarial network model for power data anonymization protection. The model first parses the original JSON file and encodes it using different feature encoders to effectively handle different types of variables. Second, the generative adversarial network is improved to generate anonymized data through loss feedback, and privacy is protected during data generation by adding random noise. Compared with the existing methods, the model is able to generate anonymized data with high utility and strong similarity to the original data for the original data of mixed data types, and realize decoupling with the original data. Experiments demonstrate that the data synthesized by the model proposed in this paper has significantly reduced differences in machine learning utility and statistical similarity compared to the original data, thus it can be used to replace the original data for mining analysis and data sharing, and effectively realize the privacy protection of the original data.

References

[1]
Guan A, Guan D J. An efficient and privacy protection communication scheme for smart grid[J]. IEEE Access, 2020, 8: 179047-179054.
[2]
Jawurek M, Johns M, Kerschbaum F. Plug-in privacy for smart metering billing[C]//International Symposium on Privacy Enhancing Technologies Symposium. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011: 192-210.
[3]
Kong W, Shen J, Vijayakumar P, A practical group blind signature scheme for privacy protection in smart grid[J]. Journal of Parallel and Distributed Computing, 2020, 136: 29-39.
[4]
Li Yuyuan, Yu Haiyang. Research on Privacy Protection Scheme for Smart Grid Based on Group Blind Signature[J]. Automation Instrumentation, 2022, 43(06): 85-89.
[5]
Li Kaixuan, Cao Lin, Du Kangning. A Sketch Face Synthesis Method Based on Dual-Generative Adversarial Networks[J]. Computer Application and Software, 2019, 36(12): 176-183.
[6]
Jang E, Gu S, Poole B. Categorical reparameterization with gumbel-softmax[J]. arXiv preprint arXiv:1611.01144, 2016.
[7]
Maddison C J, Mnih A, Teh Y W. The concrete distribution: A continuous relaxation of discrete random variables[J]. arXiv preprint arXiv:1611.00712, 2016.
[8]
Kusner M J, Hernández-Lobato J M. Gans for sequences of discrete elements with the gumbel-softmax distribution[J]. arXiv preprint arXiv:1611.04051, 2016.
[9]
Yu L, Zhang W, Wang J, Seqgan: Sequence generative adversarial nets with policy gradient[C]//Proceedings of the AAAI conference on artificial intelligence. 2017, 31(1).
[10]
Zhao J, Kim Y, Zhang K, Adversarially regularized autoencoders[C]//International conference on machine learning. PMLR, 2018: 5902-5911.
[11]
Park N, Mohammadi M, Gorde K, Data synthesis based on generative adversarial networks[J]. arXiv preprint arXiv:1806.03384, 2018.
[12]
Xu L, Skoularidou M, Cuesta-Infante A, Modeling tabular data using conditional gan[J]. Advances in neural information processing systems, 2019, 32.
[13]
Mironov I. Rényi differential privacy[C]//2017 IEEE 30th computer security foundations symposium (CSF). IEEE, 2017: 263-275.
[14]
Wei Ning, Wang Longzhi, Dong Fangmin. A Hybrid Data Generation Method Based on Generative Adversarial Networks[J]. Computer Application and Software, 2022, 39(06): 29-34.
[15]
Mirza M, Osindero S. Conditional generative adversarial nets[J]. arXiv preprint arXiv:1411.1784, 2014.
[16]
Xu L, Skoularidou M, Cuesta-Infante A, Modeling tabular data using conditional gan[J]. Advances in neural information processing systems, 2019, 32.

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  1. PDAP-GAN: Generative Adversarial Network for Power Data Anonymization Protection

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    PEAI '24: Proceedings of the 2024 International Conference on Power Electronics and Artificial Intelligence
    January 2024
    969 pages
    ISBN:9798400716638
    DOI:10.1145/3674225
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 31 July 2024

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    • Science and Technology Project of State Grid Jiangsu Electric Power Co., Ltd. Research

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