Data Leakage Assessment and Protection of News Network Platform Based on Deep Belief Network

Abstract

News network platforms are facing serious data leakage risks. Due to the lack of effective real-time assessment and protection mechanisms, user privacy and sensitive information are vulnerable to attack and abuse. To this end, this paper adopts a data leakage assessment and protection method based on deep belief network (DBN). By analyzing the user behavior data of the news platform, the potential data leakage risk is assessed. During the assessment process, the DBN model extracts features from the platform data and quantifies the leakage risk according to the risk level output by the model. Based on the risk assessment results, this paper designs a complete protection strategy, including real-time monitoring and anomaly detection based on behavioral analysis, Advanced Encryption Standard with a 256-bit key length (AES-256), Multi-factor Authentication (MFA), Role-based Access Control (RBAC), and regular vulnerability scanning, aiming to improve the platform’s security protection capabilities and prevent potential leakage incidents. Experimental results demonstrate that the accuracy of the assessment method in the risk assessment training process reaches 95.7%, and the recognition error rate of high-risk categories is only 7.5%, which effectively identifies potential risks. The protection strategy significantly reduces the security threat of the platform and ensures the privacy and security of user data. The solution proposed in this paper provides a comprehensive and effective data leakage protection framework for news network platforms.