A Novel Privacy Protection Framework for Power Generation Data based on Generative Adversarial Networks

Feng, Xinyao; Lan, Jian; Peng, Zewu; Huang, Zhisheng; Guo, Qinglai

doi:10.1109/appeec45492.2019.8994702

Cited by 5 publications

(2 citation statements)

References 12 publications

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“…Generative adversarial network (GAN) and additive correlated noise have been studied to protect smart meter consumption data [30,31]. One of the benefits of GAN is its ability to model the uncertainties of original data and based on this model a new data is generated, which can be used for grid operations such as planning and scheduling.…”

Section: Related Workmentioning

confidence: 99%

DFTMicroagg: a dual-level anonymization algorithm for smart grid data

Adewole

Torra

2022

Int. J. Inf. Secur.

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The introduction of advanced metering infrastructure (AMI) smart meters has given rise to fine-grained electricity usage data at different levels of time granularity. AMI collects high-frequency daily energy consumption data that enables utility companies and data aggregators to perform a rich set of grid operations such as demand response, grid monitoring, load forecasting and many more. However, the privacy concerns associated with daily energy consumption data has been raised. Existing studies on data anonymization for smart grid data focused on the direct application of perturbation algorithms, such as microaggregation, to protect the privacy of consumers. In this paper, we empirically show that reliance on microaggregation alone is not sufficient to protect smart grid data. Therefore, we propose DFTMicroagg algorithm that provides a dual level of perturbation to improve privacy. The algorithm leverages the benefits of discrete Fourier transform (DFT) and microaggregation to provide additional layer of protection. We evaluated our algorithm on two publicly available smart grid datasets with millions of smart meters readings. Experimental results based on clustering analysis using k-Means, classification via k-nearest neighbor (kNN) algorithm and mean hourly energy consumption forecast using Seasonal Auto-Regressive Integrated Moving Average with eXogenous (SARIMAX) factors model further proved the applicability of the proposed method. Our approach provides utility companies with more flexibility to control the level of protection for their published energy data.

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Section: Related Workmentioning

confidence: 99%

DFTMicroagg: a dual-level anonymization algorithm for smart grid data

Adewole

Torra

2022

Int. J. Inf. Secur.

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“…Other privacy-related studies include the use of masking approaches for data aggregation [10], consortium blockchain framework for electric vehicles' power trading data [11], inner product encryption (IPE) for data sharing [12], differential privacy (DP) technique for load data privacy [13], generative adversarial networks (GANs) for power generation data [14], decomposition algorithm-based decentralized transactive control for peak demand reduction and preserving data privacy [15], and Benders decomposition method for integrated power and natural gas distribution in networked energy hubs while maintaining data security [16].…”

Section: Introductionmentioning

confidence: 99%

Distributed Learning Applications in Power Systems: A Review of Methods, Gaps, and Challenges

Gholizadeh

Musilek

2021

Energies

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In recent years, machine learning methods have found numerous applications in power systems for load forecasting, voltage control, power quality monitoring, anomaly detection, etc. Distributed learning is a subfield of machine learning and a descendant of the multi-agent systems field. Distributed learning is a collaboratively decentralized machine learning algorithm designed to handle large data sizes, solve complex learning problems, and increase privacy. Moreover, it can reduce the risk of a single point of failure compared to fully centralized approaches and lower the bandwidth and central storage requirements. This paper introduces three existing distributed learning frameworks and reviews the applications that have been proposed for them in power systems so far. It summarizes the methods, benefits, and challenges of distributed learning frameworks in power systems and identifies the gaps in the literature for future studies.

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Privacy Issues in Smart Grid Data: From Energy Disaggregation to Disclosure Risk

Adewole

Torra

2022

Lecture Notes in Computer Science

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A Novel Privacy Protection Framework for Power Generation Data based on Generative Adversarial Networks

Cited by 5 publications

References 12 publications

DFTMicroagg: a dual-level anonymization algorithm for smart grid data

DFTMicroagg: a dual-level anonymization algorithm for smart grid data

Distributed Learning Applications in Power Systems: A Review of Methods, Gaps, and Challenges

Privacy Issues in Smart Grid Data: From Energy Disaggregation to Disclosure Risk

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