Social network information leakage node probability prediction based on the EDLATrust algorithm
), Abstract
Message forwarding is widely used in social network information systems. However, private information can be leaked without authorization from the information publisher. Privacy information leakage nodes need to be identified to eliminate such security risks. An estimator based distributed learning automata for trust inference (EDLATrust) is developed in this study to infer the trust level between non-directly connected nodes by reducing the number of convergence steps. The EDLATrust algorithm is combined with the XGBoost algorithm to identify privacy leakage in social network by using linear and group information transmission propagation models with three information dissemination characteristics. The algorithm predicts potential links in the information transmission chain and assists predicting information leakage points to improve the information dissemination security in social networks. Tests show that the model can effectively predict information leakage points in the information transmission chain for three real social network data sets to protect user privacy.
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