Explainable Artificial-Intelligence-Based Privacy Preservation Approach for Information Dissemination on Social Networks: An Incremental Technique

Abstract

This article aims to address the issues above by defining a social network information transmission model with the amalgamation of explainable artificial intelligence (XAI) compatible with the paranormal connection. It suggests a way of information transmission called local greedy that aids in the preservation of user privacy. Its impact acts as a buffer between the conflicting interests of privacy protection and information dissemination. Aiming at the enumeration problem of seed set selection, an incremental technique is presented for constructing seed sets to minimize time overhead; a local influence subgraph method for computing nodes is also proposed to evaluate the influence of seed set propagation rapidly. The group meets privacy protection conditions. A strategy is presented to determine the upper bound on the likelihood of a node leaking state without resorting to the time-consuming Monte Carlo approach with XAI on the crawled Sina Weibo dataset. The suggested technique is validated experimentally and by example analysis, and the findings demonstrate its usefulness.