Privacy-Preserving Data Aggregation with Dynamic Billing in Fog-Based Smart Grid

As the next-generation grid, the smart grid (SG) can significantly enhance the reliability, flexibility as well as efficiency of electricity services. To address latency and bandwidth issues during data analysis, there have been attempts to introduce fog computing (FC) in SG. However, fog computing-...

詳細記述

書誌詳細
出版年:Applied Sciences
主要な著者: Huiyong Wang, Yunmei Gong, Yong Ding, Shijie Tang, Yujue Wang
フォーマット: 論文
言語:英語
出版事項: MDPI AG 2023-01-01
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オンライン・アクセス:https://www.mdpi.com/2076-3417/13/2/748
その他の書誌記述
要約:As the next-generation grid, the smart grid (SG) can significantly enhance the reliability, flexibility as well as efficiency of electricity services. To address latency and bandwidth issues during data analysis, there have been attempts to introduce fog computing (FC) in SG. However, fog computing-based smart grid (FCSG) face serious challenges in security and privacy. In this paper, we propose a privacy-preserving data aggregation scheme that supports dynamic billing and arbitration, named PPDB. Specifically, we design a four-layer data aggregation framework which uses fog nodes (FNs) to collect and aggregate electricity consumption data encrypted under the ElGamal cryptosystem and employ distributed decryption to achieve fine-grained access and bills generation based on real-time prices. In addition, we introduce a trusted third party to arbitrate disputed bills. Detailed security analysis proves that the proposed PPDB can guarantee the confidentiality, authentication and integrity of data. Compared with related schemes, the experimental results show that the communication overhead of our scheme is reduced by at least <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>38</mn><mo>%</mo></mrow></semantics></math></inline-formula>, and the computational efficiency in the billing phase is improved by at least 40 times.
ISSN:2076-3417