Privacy-preserving deep learning for electricity consumer characteristics identification

Abstract

Deep learning models trained from smart meter data have proven to be effective in predicting socio-demographic characteristics of electricity consumers, which can help retailers provide personalized service to electricity customers. Traditionally, deep learning models are trained in a centralized manner to gather large amounts of data to ensure effectiveness and efficiency. However, gathering smart meter data in plaintext may raise privacy concerns since the data is privately owned by different retailers. This indicates an imminent need for privacy-preserving deep learning. This paper proposes several secure multi-party computation (MPC) protocols that enable deep learning training and inference for electricity consumer characteristics identification while keeping the retailer’s raw data confidential. In our protocols, the retailers secret-share their raw data to three computational servers, which implement deep learning training and inference through lightweight replicated secret sharing techniques. We implement and benchmark multiple neural network models and optimization strategies. Comprehensive experiments are conducted on the Irish Commission for Energy Regulation (CER) dataset to verify that our MPC-based protocols have comparable performance.