The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
PPFA: Privacy Preserving Fog-Enabled Aggregation in Smart Grid
Abstract
For constrained end devices in Internet of Things, such as smart meters (SMs), data transmission is an energy-consuming operation. To address this problem, we propose an efficient and privacy-preserving aggregation system with the aid of Fog computing architecture, named PPFA, which enables the intermediate Fog nodes to periodically collect data from nearby SMs and accurately derive aggregate statistics as the fine-grained Fog level aggregation. The Cloud/utility supplier computes overall aggregate statistics by aggregating Fog level aggregation. To minimize the privacy leakage and mitigate the utility loss, we use more efficient and concentrated Gaussian mechanism to distribute noise generation among parties, thus offering provable differential privacy guarantees of the aggregate statistic on both Fog level and Cloud level. In addition, to ensure aggregator obliviousness and system robustness, we put forward a two-layer encryption scheme: the first layer applies OTP to encrypt individual noisy measurement to achieve aggregator obliviousness, while the second layer uses public-key cryptography for authentication purpose. Our scheme is simple, efficient, and practical, it requires only one round of data exchange among a SM, its connected Fog node and the Cloud if there are no node failures, otherwise, one extra round is needed between a meter, its connected Fog node, and the trusted third party.
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“Mhamdi et al. [140] proposed a meta-aggregation rule called Bulyan, a two-step meta-aggregation algorithm based on the Krum and trimmed median, which filters malicious updates followed by computing the trimmed median of the remaining updates.”
From Privacy and Robustness in Federated Learning: Attacks and Defenses (2024) · cites this paper as [140]
Citations
Cites
A public key cryptosystem and a signature scheme based on discrete logarithms
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Dwork, C., McSherry, F., Nissim, K., & Smith, A. (2006) · Lecture Notes in Computer Science · doi:10.1007/11681878_14 · Cited by 7,272
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Paillier, P. (2007) · Lecture Notes in Computer Science · doi:10.1007/3-540-48910-x_16 · Cited by 7,250
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Gentry, C. (2009) · Proceedings of the forty-first annual ACM symposium on Theory of computing · doi:10.1145/1536414.1536440 · Cited by 6,806
The Algorithmic Foundations of Differential Privacy
Dwork, C., & Roth, A. (2014) · Foundations and Trends® in Theoretical Computer Science · doi:10.1561/0400000042 · Cited by 5,557
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Xu, L. D., He, W., & Li, S. (2014) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2014.2300753 · Cited by 5,172
The Algorithmic Foundations of Differential Privacy
Dwork, C., & Roth, A. (2013) · doi:10.1561/9781601988195 · Cited by 3,856
Smart Grid Technologies: Communication Technologies and Standards
Güngör, V. Ç., Sahin, D., Koçak, T., Ergüt, S., Buccella, C., Cecati, C., & Hancke, G. P. (2011) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2011.2166794 · Cited by 2,694
Our Data, Ourselves: Privacy Via Distributed Noise Generation
Dwork, C., Kenthapadi, K., McSherry, F., Mironov, I., & Naor, M. (2006) · Lecture Notes in Computer Science · doi:10.1007/11761679_29 · Cited by 1,835
EPPA: An Efficient and Privacy-Preserving Aggregation Scheme for Secure Smart Grid Communications
Lu, R., Liang, X., Li, X., Lin, X., & Shen, X. (2012) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2012.86 · Cited by 769
Research works printed but not linked: 1
- shi, 0, Privacy-preserving aggregation of time-series data, Proc Symp Network and Distributed System Security
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Sorted by citations
Khan, W. Z., Ahmed, E., Hakak, S., Yaqoob, I., & Ahmed, A. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.02.050 · Cited by 1,157
Privacy and Robustness in Federated Learning: Attacks and Defenses
Lyu, L., Yu, H., Ma, X., Chen, C., Sun, L., Zhao, J., Yang, Q., & Yu, P. S. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3216981 · Cited by 548
Self-citationDecentralized Privacy Using Blockchain-Enabled Federated Learning in Fog Computing
Qu, Y., Gao, L., Luan, T. H., Xiang, Y., Yu, S., Li, B., & Zheng, G. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2977383 · Cited by 451
Wang, J., Wu, L., Choo, K. R., & He, D. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2936278 · Cited by 301
Hazra, A., Rana, P., Adhikari, M., & Amgoth, T. (2023) · Computer Science Review · doi:10.1016/j.cosrev.2023.100549 · Cited by 264
Metadata
- Authors
- Lyu, L., Nandakumar, K., Rubinstein, B., Jin, J., Bedo, J., & Palaniswami, M.
- Affiliations
- The University of Melbourne (AU)
- IBM (United States) (US)
- Swinburne University of Technology (AU)
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2018-08
- Volume
- 14
- Issue
- 8
- Pages
- 3733-3744
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W2786010450
- Cited by
- 240
- References
- 25
- Citations by year
- 2026: 4 · 2025: 25 · 2024: 24 · 2023: 28 · 2022: 36 · 2021: 31 · 2020: 49 · 2019: 31 · 2018: 12
- FWCI
- 17.21
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Industrial Informatics (publishedVersion)
- Swinburne figshare (Swinburne University of Technology) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Engineering › Control and Systems Engineering › Smart Grid Security and Resilience
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Keywords
- privacy-preserving data aggregation, fog computing, smart grid, smart meters, Internet of Things, differential privacy, Gaussian mechanism, one-time pad encryption, public-key cryptography, energy-efficient data transmission, trusted third party
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