Smart meter achieves real-time and comprehensive collection of user's electricity consumption information. It is possible to accurately do clustering analysis of the user's electrical behavior by using such data. However

in the process

the user's information can be leaked easily. So a method of differential privacy clustering analysis for electricity information is proposed. It utilizes two phase privacy protection clustering to solve the contradiction that privacy protection and accurate analysis cannot coexist. Two phase clustering adopting the distributed computation idea consists of local clustering and global clustering. The former uses differential privacy adaptive K-means algorithm to complete first cluster electricity consumption data collected by smart meter. In global clustering

a new clustering algorithm based on density and hierarchy is designed to optimize the results from local clustering. Relevant experiments show that this method is effective.