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AuthorKserawi, Fawaz
AuthorAl-Marri, Saeed
AuthorMalluhi, Qutaibah
Available date2024-07-17T07:14:44Z
Publication Date2022
Publication NameIEEE Access
ResourceScopus
Identifierhttp://dx.doi.org/10.1109/ACCESS.2022.3167015
ISSN21693536
URIhttp://hdl.handle.net/10576/56751
AbstractThe edge of the smart grid has a massive number of power and resource-constrained interconnected devices. Mainly, smart meters report power consumption data from consumer homes, industrial buildings, and other connected infrastructures. Multiple approaches were proposed in the literature to preserve the privacy of consumers by altering the data via additive noise, masking, or other data obfuscation techniques. A significant body of work in the literature employs differential privacy methods with constraining predefined parameters to achieve the optimal trade-off between privacy and utility of the data. However, billing accuracy can be degraded by using such additive noise techniques. We propose a differentially-private model that perturbs data by adding noise obtained from a virtual chargeable battery, while maintaining billing accuracy. Our model utilizes fog-computing data aggregation with lightweight cryptographic primitives to ensure the authenticity and confidentiality of data generated by low-end devices. We describe our differentially-private model with flexible constraints and a dynamic window algorithm to maintain the privacy-budget loss in infinitely generated time-series data. Our experimental results show a possible decrease in data perturbation error by 51.7% and 61.2% for smart meters and fog-computing data aggregators perturbed data, respectively, compared to the commonly used Gaussian mechanism.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectAdvanced metering infrastructure
differential privacy
electrical grid
information privacy
smart grid
smart meter
the Internet of Things
TitlePrivacy-Preserving Fog Aggregation of Smart Grid Data Using Dynamic Differentially-Private Data Perturbation
TypeArticle
Pagination43159-43174
Volume Number10
dc.accessType Open Access


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