Differential Privacy and Multilayer Grouping Consensus Algorithm for Social Network Privacy and Security Management

Abstract

The research aims to propose a social network privacy protection scheme that combines differential privacy and multilayer grouping consensus algorithm to solve the problems of user privacy leakage and data abuse. Firstly, a community discovery data storage mechanism based on regional chains was designed to protect the security and integrity of data. Then, a multilayer grouping consensus algorithm was proposed to improve consensus efficiency through classification and hierarchical consensus. These results confirm that the proposed privacy protection scheme has improved privacy protection by about 75% and increased data availability by about 55% compared to other schemes such as Spctr Switch. When nodes are 200, compared to the traditional Byzantine consensus algorithm, the communication cost based on multilayer grouping consensus algorithm is saved by about 89.9%, and the consensus delay is reduced by about 75.6%. This research plan not only ensures user privacy and security, but also improves data availability, providing an effective method for social network privacy and security management, which helps maintain the stability and security of blockchain networks.