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The proliferation of online social networks (OSNs) has led to unprecedented sharing of personal information, creating critical privacy challenges. This research presents an innovative framework that addresses privacy concerns in social big data environments, particularly focusing on multiple heterogeneous networks. Our approach combines recommender systems with graph structure analysis to create a comprehensive privacy preservation model. The framework enables users to understand potential privacy risks and make informed decisions about sharing personal information. Through experimental validation, we demonstrate the effectiveness of our proposed algorithm in enhancing data privacy across different social networking platforms.

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Enhancing Privacy Preserving in Online Social Networks for Big Data Using Recommender System and Graph Structure Analysis

  • 会议论文

  • 发表日期：

    2025年

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  • Suneetha Davuluri,
  • Venugopal Boppana,
  • Venkata Murali Krishna Chinta,
  • Indraja Lingamaneni

摘要

The proliferation of online social networks (OSNs) has led to unprecedented sharing of personal information, creating critical privacy challenges. This research presents an innovative framework that addresses privacy concerns in social big data environments, particularly focusing on multiple heterogeneous networks. Our approach combines recommender systems with graph structure analysis to create a comprehensive privacy preservation model. The framework enables users to understand potential privacy risks and make informed decisions about sharing personal information. Through experimental validation, we demonstrate the effectiveness of our proposed algorithm in enhancing data privacy across different social networking platforms.