Privacy Protection Intensity Measure for Energy Big Data Based on Information Entropy and Group Privacy Preferences

Amidst the rapid growth of the mobile Internet, service providers have gathered extensive park energy big data and related resources via diverse end-user devices. While this data is extensively used for business enhancement and improving user experiences, it simultaneously sparks user privacy concerns. Existing research often concentrates on the effects of exposing non-sensitive user attributes in isolated data-sharing instances, frequently neglecting the importance of aligning with users' privacy preferences. Addressing this gap, this paper presents a novel privacy metric model for data sharing, grounded in the principles of information entropy and communal privacy inclinations, utilizes information theory to address the challenges associated with defining a metric for privacy, applying an advanced entropy weight algorithm for a thorough assessment of the privacy level of energy big data in integrated parks. Experimental results reveal that this privacy metric model surpasses traditional methods in accurately quantifying data privacy. It offers a robust evaluation framework for energy big data sharing and publication in integrated parks, promising significant improvements in data privacy protection.