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. proposed Bulyan, a two-step meta-aggregation based on Krum and trimmed median, which filters malicious updates followed by computing the trimmed median of the remaining updates.
“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]
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- 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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