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EnglishThe privacy-preserving management of energy consumption measurements gathered by Smart Meters plays a pivotal role in the Automatic Metering Infrastructure of Smart Grids. Grid users and standardization committees are requiring that utilities and third parties collecting aggregated metering data are prevented from accessing measurements at the household granularity, and data perturbation is a technique used to provide a trade-off between the privacy of individual users and the precision of the aggregated measurements. In this paper, we discuss a decisional attack to aggregation with data-perturbation, showing that a curious entity can exploit the temporal correlation of Smart Grid measurements to detect the presence or absence of individual data generated by a given user inside an aggregate. We also propose a countermeasure to such attack and show its effectiveness using both synthetic and real home energy consumption measurement traces.
A decisional attack to privacy-friendly data aggregation in Smart Grids / Rottondi, C.; Savi, M.; Polenghi, D.; Verticale, G.; Krausß, C.. - ELETTRONICO. - (2013), pp. 2616-2621. ((Intervento presentato al convegno 2013 IEEE Global Communications Conference, GLOBECOM 2013 tenutosi a Atlanta, GA (USA) nel 9-13 December 2013.
| Titolo: | A decisional attack to privacy-friendly data aggregation in Smart Grids |
| Autori: | |
| Data di pubblicazione: | 2013 |
| Abstract: | The privacy-preserving management of energy consumption measurements gathered by Smart Meters pla...ys a pivotal role in the Automatic Metering Infrastructure of Smart Grids. Grid users and standardization committees are requiring that utilities and third parties collecting aggregated metering data are prevented from accessing measurements at the household granularity, and data perturbation is a technique used to provide a trade-off between the privacy of individual users and the precision of the aggregated measurements. In this paper, we discuss a decisional attack to aggregation with data-perturbation, showing that a curious entity can exploit the temporal correlation of Smart Grid measurements to detect the presence or absence of individual data generated by a given user inside an aggregate. We also propose a countermeasure to such attack and show its effectiveness using both synthetic and real home energy consumption measurement traces. |
| ISBN: | 978-1-4799-1353-4 |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| decisional_globecom.pdf | articolo principale | 2a Post-print versione editoriale / Version of Record | Non Pubblico - Accesso privato/ristretto | Administrator Richiedi una copia |
http://hdl.handle.net/11583/2723349
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