Next Article in Journal
Deep Multi-Modal Metric Learning with Multi-Scale Correlation for Image-Text Retrieval
Previous Article in Journal
One Step Greener: Reducing 5G and Beyond Networks’ Carbon Footprint by 2-Tiering Energy Efficiency with CO2 Offsetting
Previous Article in Special Issue
Performance Analysis of a Grid-Connected Rooftop Solar Photovoltaic System
Open AccessFeature PaperArticle

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

1
Department of Electrical Power Systems, Helmut Schmidt University Hamburg, 22043 Hamburg, Germany
2
School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
3
Department of Engineering Science, Oxford University, Oxford OX1 2JD, UK
*
Author to whom correspondence should be addressed.
Electronics 2020, 9(3), 465; https://doi.org/10.3390/electronics9030465
Received: 28 February 2020 / Revised: 6 March 2020 / Accepted: 7 March 2020 / Published: 10 March 2020
(This article belongs to the Special Issue Grid Integration of Decentralized Generation Plants)
The encoding of high-resolution energy profile datasets from end-users generated by smart electricity meters while maintaining the fidelity of relevant information seems to be one of the backbones of smart electrical markets. In the end-user sphere of smart grids, specific load curves of households can easily be utilized to aggregate detailed information about customer’s daily activities, which would be attractive for cyber attacks. Based on a dataset measured by a smart meter installed in a German household, this paper integrates two complementary approaches to encrypt load profile datasets. On the one hand, the paper explains an integration of a lossy compression and classification technique, which is usable for individual energy consumption profiles of households. On the other hand, a perturbation approach with the Gaussian distribution is used to enhance the safety of a large amount of privacy profiles. By this complete workflow, involving the compression and perturbation, the developed framework sufficiently cut off the chance of de-noising attacks on private data and implement an additional, easy-to-handle layer of data security. View Full-Text
Keywords: encoding; data compression; privacy power/load profiles; long short-term memory classification; smart meter; perturbation encoding; data compression; privacy power/load profiles; long short-term memory classification; smart meter; perturbation
Show Figures

Figure 1

MDPI and ACS Style

Plenz, M.; Dong, C.; Grumm, F.; Meyer, M.F.; Schumann, M.; McCulloch, M.; Jia, H.; Schulz, D. Framework Integrating Lossy Compression and Perturbation for the Case of Smart Meter Privacy. Electronics 2020, 9, 465.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop