Gope, P. orcid.org/0000-0003-2786-0273 and Sikdar, B. (2019) Lightweight and privacy-friendly spatial data aggregation for secure power supply and demand management in smart grids. IEEE Transactions on Information Forensics and Security, 14 (6). pp. 1554-1566. ISSN 1556-6013
Abstract
The concept of smart metering allows real-time measurement of power demand which in turn is expected to result in more efficient energy use and better load balancing. However, finely granular measurements reported by smart meters can lead to starkly increased exposure of sensitive information, including various personal attributes and activities. Even though several security solutions have been proposed in recent years to address this issue, most of the existing solutions are based on public-key cryptographic primitives, such as homomorphic encryption and elliptic curve digital signature algorithms which are ill-suited for the resource constrained smart meters. On the other hand, to address the computational inefficiency issue, some masking-based solutions have been proposed. However, these schemes cannot ensure some of the imperative security properties, such as consumer's privacy and sender authentication. In this paper, we first propose a lightweight and privacy-friendly masking-based spatial data aggregation scheme for secure forecasting of power demand in smart grids. Our scheme only uses lightweight cryptographic primitives, such as hash functions and exclusive-OR operations. Subsequently, we propose a secure billing solution for smart grids. As compared with existing solutions, our scheme is simple and can ensure better privacy protection and computational efficiency, which are essential for smart grids.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Privacy; spatial data aggregation; smart grids |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 13 Jan 2020 16:27 |
Last Modified: | 13 Jan 2020 16:35 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Refereed: | Yes |
Identification Number: | 10.1109/tifs.2018.2881730 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:154460 |