同态加密
计算机科学
加密
云计算
期限(时间)
平均绝对百分比误差
理论计算机科学
数据挖掘
人工智能
计算机安全
人工神经网络
量子力学
操作系统
物理
作者
Liqiang Wu,Shaojing Fu,Yuchuan Luo,Ming Xu
出处
期刊:IEEE Transactions on Industrial Informatics
[Institute of Electrical and Electronics Engineers]
日期:2023-07-20
卷期号:20 (2): 2508-2518
被引量:2
标识
DOI:10.1109/tii.2023.3292532
摘要
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% $\text{ 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.
科研通智能强力驱动
Strongly Powered by AbleSci AI