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dc.creatorCao, H
dc.creatorLiu, S
dc.creatorWu, L
dc.creatorGuan, Z
dc.creatorDu, X
dc.date.accessioned2020-12-14T21:14:04Z
dc.date.available2020-12-14T21:14:04Z
dc.date.issued2019-11-25
dc.identifier.issn1532-0626
dc.identifier.issn1532-0634
dc.identifier.doihttp://dx.doi.org/10.34944/dspace/4422
dc.identifier.otherJF5GN (isidoc)
dc.identifier.urihttp://hdl.handle.net/20.500.12613/4440
dc.description.abstract© 2018 John Wiley & Sons, Ltd. Fog computing, a non-trivial extension of cloud computing to the edge of the network, has great advantage in providing services with a lower latency. In smart grid, the application of fog computing can greatly facilitate the collection of consumer's fine-grained energy consumption data, which can then be used to draw the load curve and develop a plan or model for power generation. However, such data may also reveal customer's daily activities. Non-intrusive load monitoring (NILM) can monitor an electrical circuit that powers a number of appliances switching on and off independently. If an adversary analyzes the meter readings together with the data measured by an NILM device, the customer's privacy will be disclosed. In this paper, we propose an effective privacy-preserving scheme for electric load monitoring, which can guarantee differential privacy of data disclosure in smart grid. In the proposed scheme, an energy consumption behavior model based on Factorial Hidden Markov Model (FHMM) is established. In addition, noise is added to the behavior parameter, which is different from the traditional methods that usually add noise to the energy consumption data. The analysis shows that the proposed scheme can get a better trade-off between utility and privacy compared with other popular methods.
dc.language.isoen
dc.relation.haspartConcurrency Computation
dc.relation.isreferencedbyWiley
dc.rightsAll Rights Reserved
dc.subjectdifferential privacy
dc.subjectfog computing
dc.subjectinternet of things
dc.subjectnon-intrusive load monitoring
dc.subjectsmart grid
dc.titleAchieving differential privacy against non-intrusive load monitoring in smart grid: A fog computing approach
dc.typeArticle
dc.type.genrePre-print
dc.relation.doi10.1002/cpe.4528
dc.ada.noteFor Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu
dc.creator.orcidDu, Xiaojiang|0000-0003-4235-9671
dc.date.updated2020-12-14T21:14:01Z
refterms.dateFOA2020-12-14T21:14:05Z


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