Gope, P., Sharma, P.K. and Sikdar, B. (2022) An ultra-lightweight data-aggregation scheme with deep learning security for smart grid. IEEE Wireless Communications, 29 (2). pp. 30-36. ISSN 1536-1284
Abstract
Various smart meter data aggregation protocols have been developed in the literature to address the rising privacy threats against customers' energy consumption data. However, most of these protocols require a smart meter (installed at the consumer's end) to either maintain a secret key or to run an authenticated key establishment scheme for interacting with the aggregator. Both of these approaches create additional requirements for the system. To address this issue, this article first proposes a machine-learning-based ultra-light-weight data aggregation scheme for smart grids that does not require a secret key to be maintained for communicating with the aggregator. In particular, unlike existing data aggregation schemes, in the proposed data aggregation scheme, neither the server nor the smart meter needs to store any secret. Instead, for every round of data aggregation, each smart meter uses an embedded PUF for generating a unique random response for a given challenge. On the other hand, the server maintains a PUF model for each smart meter for producing the same random response. This unique secret key is used to ensure the privacy of the metering data. Next, we propose an optimized data aggregation scheme using collaborative learning to enhance the performance of the proposed scheme.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 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: | Data privacy; Deep learning; Power demand; Data aggregation; Collaborative work |
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: | 27 Jun 2022 10:56 |
Last Modified: | 01 Apr 2023 00:13 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Refereed: | Yes |
Identification Number: | 10.1109/mwc.003.2100273 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:188420 |