Differential Private Smart Metering

Abstract: This work presents a new privacy-preserving smart metering system. Our scheme is private under the di erential privacy model and therefore provides strong and provable guarantees. With our scheme, an (electricity) supplier can periodically collect data from smart meters and derive aggregated statistics while learning only limited information about the activities of individual households. For example, a supplier cannot tell from a user’s trace when he watched TV or turned on heating. Our scheme is simple, efficient and practical. Processing cost is very limited: smart meters only have to add noise to their data and encrypt the results with an ecient stream cipher.


[WPES11] G.Acs, C.Castelluccia and W. Lecat. Protecting against Physical Resource Monitoring. The 10th ACM Workshop on Privacy in the Electronic Society (WPES), 2011 (pdf).

[IH11] G.Acs and C. Castelluccia. I have a DREAM (DiffeREntially PrivAte smart Metering ). 13th Information Hiding Conference (IH), May 2011, Prague (pdf).

[ICT12] G. Acs and C.Castelluccia. Differentially Private Smart Metering. ICT Labs deliverables. Nov. 2012 (pdf).


SimElec (SimElec-0.01.tar.gz): This is a perl implementation of the high-resolution domestic electricity demand model originally developed by Ian Richardson and Murray Thomson in VBA script. SimElec can generate one-min resolution synthetic electricity demand data (i.e., electricity consumption data) for multiple dwellings as well as the corresponding plots consisting of the overall power demand profile, the usage profile of each appliance, the lighting power demand profile, and the active occupancy profile.


This work was partially funded by a grant from the ICT Labs.

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