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dc.contributor.authorSchirmer, Pascal
dc.contributor.authorMporas, Iosif
dc.date.accessioned2021-01-28T00:09:48Z
dc.date.available2021-01-28T00:09:48Z
dc.date.issued2021-01-04
dc.identifier.citationSchirmer , P & Mporas , I 2021 , ' On the Non-Intrusive Extraction of Residents’ Privacy and Security Sensitive Information from Energy Smart Meters ' , Neural Computing and Applications . https://doi.org/10.1007/s00521-020-05608-w
dc.identifier.issn0941-0643
dc.identifier.urihttp://hdl.handle.net/2299/23782
dc.descriptionThis is a post-peer-review, pre-copyedit version of an article published in Neural Computing and Applications. The final authenticated version is available online at: https://doi.org/10.1007/s00521-020-05608-w Funding Information: This work was supported by the UA Doctoral Training Alliance ( https://www.unialliance.ac.uk/ ) for Energy in the UK. Publisher Copyright: © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature
dc.description.abstractEnergy smart meters have become very popular in monitoring and smart energy management applications. However, the acquired measurements except the energy consumption information may also carry information about the residents’ daily routine, preferences and profile. In this article, we investigate the potential of extracting information from smart meters related to residents’ security- and privacy-sensitive information. Specifically, using methodologies for load demand prediction, non-intrusive load monitoring and elastic matching, evaluation of extraction of information related to house occupancy, multimedia watching detection, socioeconomic and health profiling of residents was performed. The evaluation results showed that the aggregated energy consumption signals contain information related to residents’ privacy and security, which can be extracted from the smart meter measurements.en
dc.format.extent785337
dc.language.isoeng
dc.relation.ispartofNeural Computing and Applications
dc.subjectConsumer privacy
dc.subjectHome security
dc.subjectNon-intrusive load monitoring
dc.subjectSmart meters
dc.subjectSoftware
dc.subjectArtificial Intelligence
dc.titleOn the Non-Intrusive Extraction of Residents’ Privacy and Security Sensitive Information from Energy Smart Metersen
dc.contributor.institutionCentre for Engineering Research
dc.contributor.institutionBioEngineering
dc.contributor.institutionCommunications and Intelligent Systems
dc.contributor.institutionSchool of Physics, Engineering & Computer Science
dc.contributor.institutionDepartment of Engineering and Technology
dc.description.statusPeer reviewed
dc.date.embargoedUntil2022-01-04
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85098783011&partnerID=8YFLogxK
dc.identifier.urlhttps://rdcu.be/cc31q
rioxxterms.versionofrecord10.1007/s00521-020-05608-w
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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