Construction of a micro-application for full lifecycle data management based on a new-generation electricity consumption information collection system

Abstract

The power consumption information collection system encompasses multiple complex technical relationships, along the data flow chain, numerous data conversion links and processing activities, as well as a multitude of threat exposure surfaces, triggering sources, and uncontrollable factors. This paper proposes a complete lifecycle micro-application management system for the power consumption information collection system, aligning with the objectives and construction programs of the project. The life cycle link of collecting, storing, and sending data about electricity use measures the uncertainty of each piece of information in the binary grid protocol by using the Gini index and information entropy. The characteristics of information data are solved using Bayes’ theorem. By analyzing the users’ behavior patterns, we can prevent them from stealing access rights and other behaviors and dispose of security risks in time. In conjunction with case studies, we conduct simulation experiments to evaluate the power consumption information collection system’s security, complexity, and privacy. In the model without privacy protection, the accuracy rate of member inference attacks is about 68%. This paper’s designed system is more resilient to member inference attacks, with an accuracy rate of less than 50%, demonstrating a superior level of privacy protection for electricity consumption data. The system in this paper uses less time than the other three schemes when the number of users exceeds 2200, peaking at about 700 ms when the number of users reaches 4000.

Keywords

• information entropy
• gini index
• bayes’ theorem
• membership inference attack
• electricity consumption collection system
• full data life cycle
• 68p30