SecTCN: Privacy-Preserving Short-Term Residential Electrical Load Forecasting

Short-term residential electrical load forecasting (SRLF) as a cloud service usually requires fine-grained electricity consumption data as input. However, those data are closely related to users' lifestyles, thus bringing about privacy concerns. We adapt homomorphic encryption into temporal convolutional networks (TCN) to yield an efficient design for SRLF, named SecTCN, which preserves privacy for both user data and model parameters. First, a homomorphic-encryption-friendly model is proposed through novel Ticktock approximations. Second, secure load forecasting over the encrypted data is executed by cloud-edge collaboration. Third, a novel data representation and related ciphertext computations are proposed to accelerate forecasting, and a position shuffler is devised to protect models from equation-solving attacks. Experimental evaluations demonstrate that SecTCN reduces a root-mean-squared error by 21.75 averagely and a mean absolute percentage error by 4.22% to 22.16%, compared to unencrypted long short-term memory (LSTM) and TCN. On average, SecTCN requires only 1.10 s to make forecasting with 10.27 MB communication traffic.