Several privacy concerns about the massive deployment of smart meters have been arisen recently. Namely, it has been shown that the fine-grained temporal traces generated by these meters can be correlated with different users behaviors. A new architecture, called multi-party metering, for enabling privacy-preserving analysis of high-frequency metering data without requiring additional complexity at the smart meter side is here proposed. The idea is to allow multiple entities to get a share of the high-frequency metering data rather than the real data, where this share does not reveal any information about the real data. By aggregating the shares provided by different users and publishing the results, these entities can statistically analyze the consumption data, without disclosing sensitive information of the users. In particular, it is proposed how to implement a user profiling clustering mechanism in this architecture. The envisaged solution is tested on synthetic electricity consumption data and real gas consumption data.
|Numero di pagine||6|
|Stato di pubblicazione||Published - 2013|
All Science Journal Classification (ASJC) codes