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A Randomized Response Model For Privacy Preserving Smart Metering
Shuang Wang1, Lijuan Cui, Jialan Que
1Division of Biomedical Informatics, University of California, San Diego, San Diego, CA.
IEEE Transactions on Smart Grid
|December 18, 2012
Summary
Smart meters raise privacy issues. A new protocol enables meters to report consumption data probabilistically, protecting individual user privacy while allowing aggregate energy analysis for load serving entities (LSEs).
Area of Science:
- Computer Science
- Electrical Engineering
- Information Security
Background:
- Smart meters collect granular energy consumption data every 15 minutes.
- This data allows inference of individual user behavior patterns, posing privacy risks.
- Existing privacy-preserving methods may not be suitable for de-centralized smart meter networks.
Purpose of the Study:
- To propose a novel privacy-preserving protocol for smart meter data.
- To enable accurate aggregate energy consumption reporting without revealing individual user patterns.
- To ensure user privacy in de-centralized smart meter environments.
Main Methods:
- A probabilistic reporting protocol where meters report true consumption with a set probability.
- Development of inference algorithms for Load Serving Entities (LSEs) to reconstruct regional consumption.
- Utilizing simulated data to validate the protocol's feasibility and performance.
Main Results:
- The proposed protocol effectively obscures individual consumption patterns from LSEs.
- Aggregate energy consumption can still be accurately reconstructed for regional planning.
- Simulated data demonstrated the method's feasibility and superior performance compared to existing approaches.
Conclusions:
- The novel protocol offers a viable solution for smart meter privacy concerns.
- It balances the need for aggregate data with the protection of individual user privacy.
- This approach enhances the security and trustworthiness of smart grid technologies.
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