Name
Abstract | ||
|---|---|---|
In this paper, we propose an efficient distortion-based privacy-preserving metering scheme that protects an individual customer's privacy and provides the complete power consumption distribution curve of a multitude of customers without privacy invasion. In the proposed scheme, a random noise is purposely introduced to distort customers' power consumption data at the smart meter so that data recovery becomes infeasible. Using the power consumption data and prior knowledge about added random noise, we develop an efficient algorithm for power consumption distribution reconstruction needed for power demand analysis and prediction. As a complete solution, our scheme also supports a privacy-preserving billing service. Using experimental results from real world single household power consumption data set and synthesized data of a large number of households, we demonstrate that the proposed scheme is robust against known attacks. Since it does not demand new facilities on existing smart grids, the proposed scheme offers a practical solution. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ACCESS.2013.2260815 | IEEE Access |
Keywords | Field | DocType |
privacy-preserving billing service,power consumption,data privacy,privacy-preserving metering,smart meter,power meters,power demand analysis,distortion-based approach,customer privacy,random noise,smart power grids,distribution curve,privacy-preserving,single household,privacy protection,smart grids,automatic meter reading,privacy | Smart grid,Computer science,Random noise,Data recovery,Smart meter,Information privacy,Distortion,Metering mode,Power consumption,Distributed computing | Journal |
Volume | ISSN | Citations |
1 | 2169-3536 | 9 |
PageRank | References | Authors |
0.50 | 11 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xingze He | 1 | 25 | 1.68 |
| Zhang Xinwen | 2 | 1695 | 104.61 |
| C.-C. Jay Kuo | 3 | 7524 | 697.44 |