Name
Abstract | ||
|---|---|---|
Household smart meters that measure power consumption in real-time at fine granularities are the foundation of a future smart electricity grid. However, the widespread deployment of smart meters has serious privacy implications since they inadvertently leak detailed information about household activities. In this paper, we show that even without a priori knowledge of household activities or prior training, it is possible to extract complex usage patterns from smart meter data using off-the-shelf statistical methods. Our analysis uses two months of data from three homes, which we instrumented to log aggregate household power consumption every second. With the data from our small-scale deployment, we demonstrate the potential for power consumption patterns to reveal a range of information, such as how many people are in the home, sleeping routines, eating routines, etc. We then sketch out the design of a privacy-enhancing smart meter architecture that allows an electric utility to achieve its net metering goals without compromising the privacy of its customers. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1145/1878431.1878446 | BuildSys@SenSys |
Keywords | Field | DocType |
household activity,security,aggregate household power consumption,smart meter data,private memoir,smart meter,smart meters,future smart electricity grid,power consumption pattern,privacy,leak detailed information,measure power consumption,privacy-enhancing smart meter architecture,smart grid,household smart meter,real time,a priori knowledge | Architecture,Software deployment,Electric utility,Metre,Smart grid,Computer security,Engineering,Smart meter,Sketch,Net metering | Conference |
Citations | PageRank | References |
173 | 9.64 | 12 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andres Molina-Markham | 1 | 200 | 12.37 |
| Prashant J. Shenoy | 2 | 6386 | 521.30 |
| Kevin Fu | 3 | 2281 | 167.38 |
| Emmanuel Cecchet | 4 | 832 | 51.56 |
| David Irwin | 5 | 563 | 30.93 |