Framework Integrating Lossy Compression and Perturbation for the Case of Smart Meter Privacy

Plenz, Maik; Dong, Chaoyu; Grumm, Florian; Meyer, Marc Florian; Schumann, Marc; McCulloch, Malcolm; Jia, Hongjie; Schulz, Detlef

doi:10.3390/electronics9030465

Cited by 6 publications

(5 citation statements)

References 34 publications

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“…Nevertheless, this study established that finding an appropriate balance between efficiency and loss ratio is not a trivial issue when applying compression algorithms on smart meter data. Similar findings have also been presented in [28,29] based on smart meter data compression.…”

Section: Related Worksupporting

confidence: 86%

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 Worksupporting

confidence: 86%

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

Adewole

Torra

2022

Int. J. Inf. Secur.

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“…In the actual energy consumption information collection system, the measured value of the total table is defined as the energy consumption of the total station, which is because the accuracy of the total meter in the station area is higher than the accuracy of the countersupported meter. It is assumed that there is no measurement error in the entire meter, and it is also assumed that the weighted average of the relative errors remains stable within several consecutive measurement periods [21,22]. In addition, determining the correct relationship between household changes is a basic requirement for theoretical calculations.…”

Section: Related Theories and Methods For Thementioning

confidence: 99%

Reliability Analysis of Intelligent Electric Energy Meter under Fusion Model Illness Analysis Algorithm

Xie

Zhang

et al. 2021

Journal of Sensors

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This work is aimed at solving the morbidity problem of the smart meter fusion model and improve the measurement accuracy and reliability of the smart meter. Starting with the topology of the smart meter, the reason for the serious morbidity of the smart meter model is discussed. First, the basic process of power system state estimation of smart meters is introduced, and the concept of error analysis of smart meters is clarified. Then, the causes and mechanisms of the ill-conditioned problems of the smart meter model are analyzed, and methods to reduce the morbidity of the smart meter calculation model are analyzed. Finally, a data optimization algorithm based on a greedy strategy and an improved Tikhonov regularization method is proposed. The model data is processed and optimized to reduce the morbidity of the smart meter measurement model. The results show that the analysis algorithm for reducing the morbidity error of the smart meter proposed in this study can effectively interfere with the morbidity of the smart meter calculation model. The processing effect shows that it can reduce the measurement error of the smart meter to about 5%, which is an order of magnitude lower than the error before processing, and the processing effect of the least square method is improved by more than 70%. From the perspective of processing speed, when the user number is between 50 and 100, the running time of the algorithm ranges between 1.5 and 3.5 s, which can be fully adapted to the actual situation and has strong practicability. In short, this study is helpful in improving the accuracy and reliability of smart meter calculations and provides a certain reference for related research.

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“…Hereinafter follows a short description of the lossy compression methods that are compared in this paper. All of these approaches are frequently used for other types of data (SVD, WT) and appear interesting for the paper approach (TFA) [17][18][19][20][21][22]. The two well-known, widely used approaches WT and SVD are only briefly described.…”

Section: Lossy Compression Techniquesmentioning

confidence: 99%

“…For further explanation, please consult the references that are listed in the Sections 3.1 and 3.2. TFA is explained in more detail but can be found in [17,22] if necessary.…”

Section: Lossy Compression Techniquesmentioning

confidence: 99%

Impact of Lossy Compression Techniques on the Impedance Determination

et al. 2020

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One of the essential parameters to measure the stability and power-quality of an energy grid is the network impedance. Including distinct resonances which may also vary over time due to changing load or generation conditions in a network, the frequency characteristic of the impedance is an import part to analyse. The determination and analysis of the impedance go hand in hand with a massive amount of data output. The reduction of this high-resolution voltage and current datasets, while maintaining the fidelity of important information, is the main focus of this paper. The presented approach takes measured impedance datasets and a set of lossy compression procedures, to monitor the performance success with known key metrics. Afterwards, it continually compares the results of various lossy compression techniques. The innovative contribution is the combination of new and existing procedures as well as metrics in one approach, to reduce the size of the impedance datasets for the first time. The approach needs to be efficient, suitable, and exact, otherwise the decompression results are useless.

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Framework Integrating Lossy Compression and Perturbation for the Case of Smart Meter Privacy

Cited by 6 publications

References 34 publications

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

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

Reliability Analysis of Intelligent Electric Energy Meter under Fusion Model Illness Analysis Algorithm

Impact of Lossy Compression Techniques on the Impedance Determination

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