A Technique to provide differential privacy for appliance usage in smart metering

Smart meters read electric usage and enable power providers to collect detailed consumption data from consumers. Based on this data, power providers can perform and improve many services such as differential tariffs and load monitoring. However, these readings also gather personal information that can be intrusive and threaten consumers' privacy. Consequently there is an urgent need to address how to protect consumers' privacy when using smart meter systems. We propose a lightweight approach for offering privacy using noise addition. Since the consumer behavior is very correlated with appliance usage, we measure the privacy level achieved by appliances through the state of the art in privacy (i.e., differential privacy model) and evaluate a filtering attack to eliminate the added noise. The utility is validated in a discussion regarding the smart meter benefits and an evaluation if they can still be provided when using our proposed approach.