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Efficient and privacy-preserving data aggregation and dynamic billing in smart grid metering networks

Abstract:
The smart grid enables convenient data collection between smart meters and operation centers via data concentrators. However, it presents security and privacy issues for the customer. For instance, a malicious data concentrator can not only use consumption data for malicious purposes but also can reveal life patterns of the customers. Recently, several methods in different groups (e.g., secure data aggregation, etc.) have been proposed to collect the consumption usage in a privacy-preserving manner. Nevertheless, most of the schemes either introduce computational complexities in data aggregation or fail to support privacy-preserving billing against the internal adversaries (e.g., malicious data concentrators). In this paper, we propose an efficient and privacy-preserving data aggregation scheme that supports dynamic billing and provides security against internal adversaries in the smart grid. The proposed scheme actively includes the customer in the registration process, leading to end-to-end secure data aggregation, together with accurate and dynamic billing offering privacy protection. Compared with the related work, the scheme provides a balanced trade-off between security and efficacy (i.e., low communication and computation overhead while providing robust security).
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.3390/en11082085

Authors


More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Computer Science
Role:
Author


More from this funder
Funding agency for:
Kumar, P
Martin, A
Grant:
NRF2015NCR-NCR003-003
NRF2015NCR-NCR003-003
More from this funder
Funding agency for:
Kumar, P
Martin, A
Grant:
NRF2015NCR-NCR003-003
NRF2015NCR-NCR003-003


Publisher:
MDPI
Journal:
Energies More from this journal
Volume:
11
Issue:
8
Pages:
2085
Publication date:
2018-08-10
Acceptance date:
2018-08-01
DOI:
EISSN:
1996-1073


Keywords:
Pubs id:
pubs:892564
UUID:
uuid:578fd2bc-1c90-43aa-8360-f75787561a95
Local pid:
pubs:892564
Source identifiers:
892564
Deposit date:
2018-08-03

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