Due to the tremendous growth of online social networks in both participants and collected contents, social data publication has provided an opportunity for numerous services. However, neglectfully publishing all the contents leads to severe disclosure of sensitive information due to diverse user behaviors. Therefore, there should be a thoroughly designed framework for data publication in online social networks that considers users heterogeneous privacy preferences and the correlations among participants. This work proposes a novel mechanism for data publication that achieves high performance while preserving privacy and guaranteeing fairness among users. To derive the optimal scheme for data publication is NP-complete. Thus we propose a heuristic algorithm to determine the contents to be published which takes advantage of the sets of sensitive contents for each user and the correlation among them. The theoretical analysis proves the effectiveness and feasibility of the mechanism. The evaluations towards a real-world dataset reveal that the proposed algorithm outperforms the existing results.

中文翻译：

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在线社交网络中隐私数据发布的公平机制

由于参与者和收集内容的在线社交网络的巨大增长，社交数据发布为众多服务提供了机会。然而，由于用户行为的多样性，疏忽地发布所有内容会导致敏感信息的严重泄露。因此，应该有一个彻底设计的在线社交网络数据发布框架，该框架考虑到用户异构隐私偏好和参与者之间的相关性。这项工作提出了一种新的数据发布机制，可以在保护隐私和保证用户公平性的同时实现高性能。导出数据发布的最佳方案是 NP 完全的。因此，我们提出了一种启发式算法来确定要发布的内容，该算法利用了每个用户的敏感内容集及其之间的相关性。理论分析证明了该机制的有效性和可行性。对真实世界数据集的评估表明，所提出的算法优于现有结果。