Mitigating consumer privacy breach in smart grid using obfuscation-based generative adversarial network.

Smart meters allow real-time monitoring and collection of power consumption data of a consumer's premise. With the worldwide integration of smart meters, there has been a substantial rise in concerns regarding threats to consumer privacy. The exposed fine-grained power consumption data results in behaviour leakage by revealing the end-user's home appliance usage information. Previously, researchers have proposed approaches to alter data using perturbation, aggregation or hide identifiers using anonymization. Unfortunately, these techniques suffer from various limitations. In this paper, we propose a privacy preserving architecture for fine-grained power data in a smart grid. The proposed architecture uses generative adversarial network (GAN) and an obfuscator to generate a synthetic timeseries. The proposed architecture enables to replace the existing appliance signature with appliances that are not active during that period while ensuring minimum energy difference between the ground truth and the synthetic timeseries. We use real-world dataset containing power consumption readings for our experiment and use non-intrusive load monitoring (NILM) algorithms to show that our approach is more effective in preserving the privacy level of a consumer's power consumption data.