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User daily behavior disturbance model to preserve privacy for smart metering

By applying the knowledge of customer power usage statistics from smart meters, power companies are able to improve the quality and efficiency of power delivery. However, using electrical usage data bears privacy issues. This study presents a novel user daily behavior disturbance algorithm (UDBD) to provide a privacy preserving mechanism for smart meters. Utilizing the user previous power usage data, UDBD makes slight changes to current power usage as needed. Experiment results show that UDBD outperforms the Gaussian random noise approach. UDBD is a practical smart meter privacy preserving tool which ensures privacy protection with minimum total data distortion.