Journal ArticleDOI
Smart Meter Privacy: A Theoretical Framework
TLDR
A new framework is presented that abstracts both the privacy and the utility requirements of smart meter data and exploits the presence of high-power but less private appliance spectra as implicit distortion noise to create an optimal privacy-preserving solution.Abstract:
The solutions offered to-date for end-user privacy in smart meter measurements, a well-known challenge in the smart grid, have been tied to specific technologies such as batteries or assumptions on data usage without quantifying the loss of benefit (utility) that results from any such approach. Using tools from information theory and a hidden Markov model for the measurements, a new framework is presented that abstracts both the privacy and the utility requirements of smart meter data. This leads to a novel privacy-utility tradeoff problem with minimal assumptions that is tractable. For a stationary Gaussian model of the electricity load, it is shown that for a desired mean-square distortion (utility) measure between the measured and revealed data, the optimal privacy-preserving solution: i) exploits the presence of high-power but less private appliance spectra as implicit distortion noise, and ii) filters out frequency components with lower power relative to a distortion threshold; this approach encompasses many previously proposed approaches to smart meter privacy.read more
Citations
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Book ChapterDOI
Comparison of Computer Vision Approaches in Application to the Electricity and Gas Meter Reading
TL;DR: This chapter presents comparison of computer vision approaches in application to the meter reading process for the standard (non-smart) electricity and gas, and analyses four techniques, Google Cloud Vision, AWS Rekognition, Tesseract OCR, and Azure's Computer Vision.
Proceedings ArticleDOI
Privacy Measures and Storage Technologies for Battery-Based Load Hiding - an Overview and Experimental Study
TL;DR: An overview of privacy measures proposed for this scenario, available storage technologies, and datasets used for the assessment of battery based load hiding are given, and a study of how these factors influence the ratings of several state-of-the-art BBLH algorithms is conducted.
Proceedings ArticleDOI
Evaluation of utility-privacy trade-offs of data manipulation techniques for smart metering
TL;DR: This work identifies appliance-level utility as well as privacy metrics and quantitatively evaluate the effectiveness of two data manipulation techniques: down-sampling and noise addition, and combines the two techniques and evaluates the levels of appliance-specific information leakage supported under miscellaneous configurations.
Journal ArticleDOI
Location-Based Optimized Service Selection for Data Management with Cloud Computing in Smart Grids
TL;DR: A customer-aware power regulatory model is proposed that provides awareness to the consumer regarding the usage of electrical energy, in a secure and reliable solution that combines the features of electrical engineering with cloud computing to ensure better performance in notifying issues, which is done based on location and enhances the operation of smart grids.
References
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Journal ArticleDOI
Nonintrusive appliance load monitoring
TL;DR: In this paper, a nonintrusive appliance load monitor that determines the energy consumption of individual appliances turning on and off in an electric load, based on detailed analysis of the current and voltage of the total load, as measured at the interface to the power source is described.
Proceedings ArticleDOI
Smart Grid Privacy via Anonymization of Smart Metering Data
TL;DR: The method described in this paper provides a 3rd party escrow mechanism for authenticated anonymous meter readings which are difficult to associate with a particular smart meter or customer.
Proceedings Article
Unsupervised disaggregation of low frequency power measurements
TL;DR: This work investigates the effectiveness of several unsupervised disaggregation methods on low frequency power measurements collected in real homes and indicates that a conditional factorial hidden semi-Markov model, which integrates additional features related to when and how appliances are used in the home and more accurately represents the power use of individual appliances, outperforms the other unsuper supervision methods.
Proceedings ArticleDOI
Secure Information Aggregation for Smart Grids Using Homomorphic Encryption
Fengjun Li,Bo Luo,Peng Liu +2 more
TL;DR: A distributed incremental data aggregation approach, in which data aggregation is performed at all smart meters involved in routing the data from the source meter to the collector unit, which is especially suitable for smart grids with repetitive routine data aggregation tasks.