Intermittently differential privacy in smart meters via rechargeable batteries

The smart meters have been widely deployed to monitor customer usage profiles around the world, which can read the energy load of residents at the rate of per minute or per second. This fine-grained data would expose personal behaviors and other sensitive information to malicious adversaries. To address this privacy concern, battery-based load hiding (BLH) scheme was proposed and had been explored for several years. The differential privacy is a provable method to preserve the privacy against the adversaries of arbitrary computational power. At first, a battery-based intermittently differential privacy (IDP) scheme is proposed in this article and also prove that the IDP can achieve the differential privacy. Then, we develop another scheme - a Reinforcement Learning (RL) algorithm to guide the battery control policy to match the requirement of battery constraints and cost saving in a better way. Finally, we integrate the IDP scheme with the RL algorithm into a complete RL-IDP scheme. The experimental results show that the privacy-preserving level performs well by our RL-IDP scheme while it can achieve cost-saving.

Automated summary

The article introduces a battery-based intermittently differential privacy (IDP) scheme and a Reinforcement Learning (RL) algorithm, integrated into an RL-IDP scheme which can protect privacy while achieving cost-saving.