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@article{ca1e6e934c4f4efa8e6012834bb0798a,
title = "On the Non-Intrusive Extraction of Residents{\textquoteright} Privacy and Security Sensitive Information from Energy Smart Meters",
abstract = "Energy 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{\textquoteright} daily routine, preferences and profile. In this article, we investigate the potential of extracting information from smart meters related to residents{\textquoteright} 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{\textquoteright} privacy and security, which can be extracted from the smart meter measurements.",
keywords = "Consumer privacy, Home security, Non-intrusive load monitoring, Smart meters",
author = "Pascal Schirmer and Iosif Mporas",
note = "This 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: {\textcopyright} 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature",
year = "2021",
month = jan,
day = "4",
doi = "10.1007/s00521-020-05608-w",
language = "English",
journal = "Neural Computing and Applications",
issn = "0941-0643",
publisher = "Springer London",

}

RIS

TY - JOUR

T1 - On the Non-Intrusive Extraction of Residents’ Privacy and Security Sensitive Information from Energy Smart Meters

AU - Schirmer, Pascal

AU - Mporas, Iosif

N1 - This 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

PY - 2021/1/4

Y1 - 2021/1/4

N2 - Energy 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.

AB - Energy 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.

KW - Consumer privacy

KW - Home security

KW - Non-intrusive load monitoring

KW - Smart meters

UR - http://www.scopus.com/inward/record.url?scp=85098783011&partnerID=8YFLogxK

U2 - 10.1007/s00521-020-05608-w

DO - 10.1007/s00521-020-05608-w

M3 - Article

JO - Neural Computing and Applications

JF - Neural Computing and Applications

SN - 0941-0643

ER -