A Fair Mechanism for Private Data Publication in Online Social Networks
A Fair Mechanism for Private Data Publication in Online Social Networks
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DOI：
10.1109/tnse.2018.2801798
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发表时间：
2020-04
影响因子：
6.6
通讯作者：
Xu Zheng;Guangchun Luo;Zhipeng Cai
中科院分区：
文献类型：
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作者：
Xu Zheng;Guangchun Luo;Zhipeng Cai
由于在线社交网络在参与者和收集的内容方面的巨大增长，社交数据发布为许多服务提供了机会。然而，由于用户行为的多样性，忽视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框架，在网上社交网络，考虑用户的异构隐私偏好和参与者之间的相关性的数据发布。这项工作提出了一种新颖的数据发布机制，可以实现高性能，同时保护隐私并保证用户之间的公平性。导出数据发布的最优方案是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，它利用了每个用户的敏感内容集和它们之间的相关性。理论分析证明了该机制的有效性和可行性。对真实数据集的评估表明，该算法优于现有的结果。
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.