Privacy-preserving and efficient aggregation based on blockchain for power grid communications in smart communities
Open accessAbstract
Intelligence is one of the most important aspects in the development of our future communities. Ranging from smart home to smart building to smart city, all these smart infrastructures must be supported by intelligent power supply. Smart grid is proposed to solve all challenges of future electricity supply. In smart grid, in order to realize optimal scheduling, an SM is installed at each home to collect the near-real-time electricity consumption data, which can be used by the utilities to offer better smart home services. However, the near-real-time data may disclose a user's private information. An adversary may track the application usage patterns by analyzing the user's electricity consumption profile. In this article, we propose a privacy-preserving and efficient data aggregation scheme. We divide users into different groups, and each group has a private blockchain to record its members' data. To preserve the inner privacy within a group, we use pseudonyms to hide users' identities, and each user may create multiple pseudonyms and associate his/ her data with different pseudonyms. In addition, the bloom filter is adopted for fast authentication. The analysis shows that the proposed scheme can meet the security requirements and achieve better performance than other popular methods.
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Applying blockchain to resource management, data sharing, or resource allocation improves edge computing performance while guaranteeing security properties of the network.
“Moreover, to achieve a trustworthy and efficient edge computing system, the blockchain functionality is applied to the resource management [83], data sharing [84] or resource allocation [85], all of which improve edge computing performances while guaranteeing security properties of the network.”
From Blockchain for 5G and beyond networks: A state of the art survey (2020) · cites this paper as [83]
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An Optimizing and Differentially Private Clustering Algorithm for Mixed Data in SDN-Based Smart Grid
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- Authors
- Guan, Z., Si, G., Zhang, X., Wu, L., Guizani, N., Du, X., & Ma, Y.
- Affiliations
- North China Electric Power University (CN)
- University of Electronic Science and Technology of China (CN)
- Fayetteville State University (US)
- Purdue University West Lafayette (US)
- Temple University (US)
- Venue
- IEEE Communications Magazine
- Published
- 2018-07
- Volume
- 56
- Issue
- 7
- Pages
- 82-88
- ISSN
- 0163-6804 (ISSN-L)
- 1558-1896
- OpenAlex
- W2806199268
- Cited by
- 367
- References
- 15
- Citations by year
- 2026: 7 · 2025: 20 · 2024: 36 · 2023: 40 · 2022: 42 · 2021: 72 · 2020: 70 · 2019: 60 · 2018: 20
- FWCI
- 89.51
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Communications Magazine (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (61402171, 2016MS29)
- Fundamental Research Funds for the Central Universities (61402171, 2015ZD12, 2016MS29)
- Topics
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Physical Sciences › Engineering › Control and Systems Engineering › Smart Grid Security and Resilience
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Keywords
- privacy-preserving data aggregation, blockchain, smart grid, electricity consumption data, smart communities, pseudonyms, Bloom filter, data authentication, user privacy, home automation
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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