PORTO @ 
Archivio Istituzionale della Ricerca
IRIS è la soluzione IT che facilita la raccolta e la gestione dei dati relativi alle attività e ai prodotti della ricerca. Fornisce a ricercatori, amministratori e valutatori gli strumenti per monitorare i risultati della ricerca, aumentarne la visibilità e allocare in modo efficace le risorse disponibili.
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.
| 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 |
File in questo prodotto:
| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| decisional_globecom.pdf | articolo principale | 2a. Post-print Versione editoriale | Non Pubblico - Accesso privato/ristretto | Administrator Richiedi una copia |
http://hdl.handle.net/11583/2723349
Copyright © 2019