Differential Privacy Preservation for Smart Meter Systems

Wu, J; Qiang, W; Zhu, T; Jin, H; Xu, P; Shen, S

Differential Privacy Preservation for Smart Meter Systems

Wu, J Qiang, W Zhu, T

Jin, H Xu, P Shen, S

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Publisher:: Springer International Publishing
Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2020, 11944 LNCS, pp. 669-685
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Wu, J
dc.contributor.author	Qiang, W
dc.contributor.author	Zhu, T https://orcid.org/0000-0003-3411-7947
dc.contributor.author	Jin, H
dc.contributor.author	Xu, P
dc.contributor.author	Shen, S https://orcid.org/0000-0003-4734-1008
dc.date.accessioned	2021-05-05T05:33:47Z
dc.date.available	2021-05-05T05:33:47Z
dc.date.issued	2020-01-01
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2020, 11944 LNCS, pp. 669-685
dc.identifier.isbn	9783030389901
dc.identifier.issn	0302-9743
dc.identifier.issn	1611-3349
dc.identifier.uri	http://hdl.handle.net/10453/148695
dc.description.abstract	With the rapid development of IoT and smart homes, smart meters have received extensive attention. The third-party applications, such as smart home controlling, dynamic demand-response, power monitoring, etc., can provide services to users based on consumption data of household electricity collected from smart meters. With the emergence of non-intrusive load monitoring, privacy issues from the data of smart meters become more and more severe. Differential privacy is a recognized concept that has become an important standard of privacy preservation for data with personal information. However, the existing privacy protection methods for the data of smart meters that are based on differential privacy sacrifices the actual energy consumption to protect the privacy of users, thus affecting the charging of power suppliers. To solve this problem, we propose a group-based noise adding method, so as to ensure the correct electricity billing. The experiments with two real-world data sets demonstrate that our approach can not only provide a strict privacy guarantee but also improve performance significantly.
dc.language	en
dc.publisher	Springer International Publishing
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.isbasedon	10.1007/978-3-030-38991-8_44
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Differential Privacy Preservation for Smart Meter Systems
dc.type	Conference Proceeding
utslib.citation.volume	11944 LNCS
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2021-05-05T05:33:45Z
pubs.publication-status	Published
pubs.volume	11944 LNCS

Abstract:

With the rapid development of IoT and smart homes, smart meters have received extensive attention. The third-party applications, such as smart home controlling, dynamic demand-response, power monitoring, etc., can provide services to users based on consumption data of household electricity collected from smart meters. With the emergence of non-intrusive load monitoring, privacy issues from the data of smart meters become more and more severe. Differential privacy is a recognized concept that has become an important standard of privacy preservation for data with personal information. However, the existing privacy protection methods for the data of smart meters that are based on differential privacy sacrifices the actual energy consumption to protect the privacy of users, thus affecting the charging of power suppliers. To solve this problem, we propose a group-based noise adding method, so as to ensure the correct electricity billing. The experiments with two real-world data sets demonstrate that our approach can not only provide a strict privacy guarantee but also improve performance significantly.

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http://hdl.handle.net/10453/148695