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Smart Grid Privacy via Anonymization of Smart Metering Data

Efthymiou, Costas, Kalogridis, Georgios·2010·2010 First IEEE International Conference on Smart Grid Communications

Abstract

The security and privacy of future smart grid and smart metering networks is important to their rollout and eventual acceptance by the public: research in this area is ongoing and smart meter users will need to be reassured that their data is secure. This paper describes a method for securely anonymizing frequent (for example, every few minutes) electrical metering data sent by a smart meter. Although such frequent metering data may be required by a utility or electrical energy distribution network for operational reasons, this data may not necessarily need to be attributable to a specific smart meter or consumer. It does, however, need to be securely attributable to a specific location (e.g. a group of houses or apartments) within the electricity distribution network. The method described in this paper provides a 3rd party escrow mechanism for authenticated anonymous meter readings which are difficult to associate with a particular smart meter or customer. This method does not preclude the provision of attributable metering data that is required for other purposes such as billing, account management or marketing research purposes.

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Metadata

Authors
Efthymiou, C., & Kalogridis, G.
Venue
2010 First IEEE International Conference on Smart Grid Communications
Publisher
IEEE
Published
2010-10
Pages
238-243
OpenAlex
W1975153313
Cited by
607
References
0
FWCI
82.57
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Smart Grid Privacy via Anonymization of Smart Metering Data. https://app.cochat.ai/papers/10.1109/smartgrid.2010.5622050
The paper
Efthymiou, C., & Kalogridis, G. (2010). Smart Grid Privacy via Anonymization of Smart Metering Data. 2010 First IEEE International Conference on Smart Grid Communications. https://doi.org/10.1109/smartgrid.2010.5622050

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.