Secure and privacy-preserving concentration of meeting data in AMI networks

Saxena, Neetesh ORCID: https://orcid.org/0000-0002-6437-0807, Choi, Bong Jun and Grijalva, Santiago 2017. Secure and privacy-preserving concentration of meeting data in AMI networks. Presented at: IEEE International Conference on Communications (ICC), Paris, France, 21-25 May 2017. 2017 IEEE International Conference on Communications (ICC). IEEE, pp. 1-7. 10.1109/ICC.2017.7996874

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Abstract

The industry has recognized the risk of cyber-attacks targeting to the advanced metering infrastructure (AMI). A potential adversary can modify or inject malicious data, and can perform security attacks over an insecure network. Also, the network operators at intermediate devices can reveal private information, such as the identity of the individual home and metering data units, to the third-party. Existing schemes generate large overheads and also do not ensure the secure delivery of correct and accurate metering data to all AMI entities, including data concentrator at the utility and the billing center. In this paper, we propose a secure and privacy-preserving data aggregation scheme based on additive homomorphic encryption and proxy re-encryption operations in the Paillier cryptosystem. The scheme can aggregate metering data without revealing the actual individual information (identity and energy usage) to intermediate entities or to any third-party, hence, resolves identity and related data theft attacks. Moreover, we propose a scalable algorithm to detect malicious metering data injected by the adversary. The proposed scheme protects the system against man-in-the-middle, replay, and impersonation attacks, and also maintains message integrity and undeniability. Our performance analysis shows that the scheme generates manageable computation, communication, and storage overheads and has efficient execution time suitable for AMI networks.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	IEEE
ISBN:	9781468390002
ISSN:	1938-1883
Date of First Compliant Deposit:	24 February 2020
Last Modified:	26 Oct 2022 08:13
URI:	https://orca.cardiff.ac.uk/id/eprint/126916

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