Privacy Protection of Power Consumption Big Data Based on Empirical Mode Decomposition and Homomorphic Encryption

Smart grid can use a large amount of user electricity data to achieve grid load balancing and power supply adjustment, but the collection process of electricity consumption information also faces the risk of divulging user privacy. Aiming at this problem, a privacy protection method based on empirical mode decomposition (EMD) and homomorphic encryption is proposed. First, the original power consumption data of user L1 is decomposed into local feature components C1, C2, …, Cd at different time scales by using an empirical mode decomposition method. Next, use the public key pub2,…,pubd of user L2, …, Ld to perform homomorphic encryption on C2, …, Cd respectively, and send the encrypted result to the data aggregator. Then, user L1 decrypts the received result with its own private key and sums with C1. User L1 sends the result to the control center with its private key signature. The control center uses the authenticity of the user L1 public key verification result. Other users are the same as user L1. Finally, the control center adds all the verified user results to obtain the total power consumption of all users at a certain moment, and cannot obtain the original power consumption data of each user, thus playing a role of privacy protection. In order to prove the effectiveness of the proposed method, using the Irish electricity company's smart meter data for simulation experiments; the experimental results show that the proposed method can effectively protect the privacy of user power data. © 2019, Power System Technology Press. All right reserved.