Fault-Tolerant Privacy-Preserving Data Aggregation for Smart Grid
Open accessAbstract
In smart grids (SG), data aggregation is widely used to strike a balance between data usability and privacy protection. The fault tolerance is an important requirement to improve the robustness of data aggregation protocols, which enables normal execution of the protocols even with failures on some entities. However, to achieve fault tolerance, most schemes either sacrifice the aggregation accuracy due to the use of differential privacy or substitution strategy or need to rely on an online trusted entity to manage all user blinding factors. In this paper, a (k,n) threshold privacy-preserving data aggregation scheme named (k,n)-PDA is proposed, which reconciles data usability and data privacy through the BGN cryptosystem and achieves fault tolerance with accurate aggregation using Shamir’s secret sharing without any online trusted entity. Besides, our scheme supports the efficient changing of users’ membership. Specifically, the dynamic secrete key is distributed to n smart meters (SMs) through the threshold secret sharing algorithm. When k or more meters participate in the aggregation, the data service center (DSC) can reconstruct the key to compute the aggregate results, and less than k SMs cannot recover the key. Thus, our solution still works functionally even if up to n−k SMs fail; also, it resists attacks from the collusion of less than k SMs. Moreover, system and performance analyses demonstrate that our scheme achieves privacy, fault tolerance, and membership dynamics with high efficiency.
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Existing differential privacy methods can set a privacy probability threshold to select the best privacy parameter based on Laplace distribution and Privacy Fault Tolerance, achieving privacy protection while ensuring data utility.
“Existing differential privacy methods, such as methods [183], [184], can set a privacy probability threshold to select the best privacy parameter based on Laplace distribution and Privacy Fault Tolerance and thus achieve privacy protection while ensuring data utility.”
From A Critical Overview of Industrial Internet of Things Security and Privacy Issues Using a Layer-Based Hacking Scenario (2023) · cites this paper as [183]
Section 2 (Related Works; page unavailable) says fault-tolerant differential-privacy schemes add random noise to protect data and produce approximate aggregation results. It does not describe a privacy-probability threshold, selecting an optimal privacy parameter, or a Laplace distribution. The paper’s own threshold scheme instead uses Shamir’s secret sharing, as described in the Abstract (page unavailable). (LLM, conf=0.86)
Experimental — accuracy not yet measuredExisting differential privacy algorithms often suffer from optimization problems in finding the best privacy parameter epsilon.
“However, the algorithm often suffers from optimization problems in finding the best privacy parameter ϵ.”
From A Critical Overview of Industrial Internet of Things Security and Privacy Issues Using a Layer-Based Hacking Scenario (2023) · cites this paper as [183]
The Related Works section describes how differential-privacy schemes add noise, and the Abstract says such schemes can sacrifice aggregation accuracy. Neither passage addresses difficulty optimizing or choosing the privacy parameter epsilon. Searches for epsilon optimization and privacy-budget selection found no passage supporting that point; page numbers are unavailable in our copy. (LLM, conf=0.73)
Experimental — accuracy not yet measured
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Cited by
Sorted by citations
Eyeleko, A. H., & Feng, T. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3308195 · Cited by 38 · see citing works
“1) Problem − 3 (Optimization): Existing differential privacy methods, such as methods [182], [183], can set a privacy probability threshold to select the best privacy parameter based on the Laplace distribution and Privacy Fault Tolerance and, thus, achieve privacy protection while ensuring data…”
Method
- Authors
- Liu, H., Gu, T., Liu, Y., Song, J., & Zeng, Z.
- Affiliations
- Xidian University (CN)
- Guilin University of Electronic Technology (CN)
- Corresponding author
- Liu, Huadong
- Liu, Yining
- Venue
- Wireless Communications and Mobile Computing
- Publisher
- Wiley
- Published
- 2020-09-30
- Volume
- 2020
- Pages
- 1-10
- ISSN
- 1530-8669 (ISSN-L)
- 1530-8677
- OpenAlex
- W3090278254
- Cited by
- 17
- References
- 19
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 3 · 2023: 4 · 2022: 2 · 2021: 3
- FWCI
- 0.88
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Wireless Communications and Mobile Computing (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Key Projects of Guangxi Natural Science Foundation (2018JJD170004, 61662016)
- National Natural Science Foundation of China (2018JJD170004, 61662016)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Cryptography and Data 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, smart grid, fault tolerance, threshold secret sharing, Shamir secret sharing, smart meters, collusion resistance, threshold cryptography, data privacy
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