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首页> 外文期刊>Computational Social Systems, IEEE Transactions on >Community Detection in Online Social Networks: A Differentially Private and Parsimonious Approach
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Community Detection in Online Social Networks: A Differentially Private and Parsimonious Approach

机译：在线社交网络中的社区检测：一种差异私下和令人置信的方法

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摘要

Community detection is an effective approach to unveil relationships among individuals in online social networks. In the literature, quite a few algorithms have been proposed to conduct community detection by exploiting the topology of social networks and the attributes of social actors. In practice, community detection is usually conducted by third parties, such as advertisement companies and hospitals, with access to social networks for different purposes, which can easily lead to a privacy breach. In this paper, we investigate community detection in social networks aiming to protect the privacy of both the network topology and the users' attributes. We show that with additional prior knowledge, community detection can be performed by querying the information of only a fraction of instead of the entire population. In particular, we first propose a new scheme called differentially private community detection (DPCD). DPCD detects communities in social networks via a probabilistic generative model, which can be decomposed into subproblems solved by individual users. The private social relationships and attributes of each user are protected by objective perturbation with differential privacy guarantees. Then, we propose a parsimonious node affiliation recovery (NAR) algorithm, which is also differentially private, to unveil the community affiliation information of the whole population based on that of the limited number of queried individuals by solving a sparse optimization problem. Through both theoretical analysis and experimental validation using synthetic and real-world social networks, we demonstrate that the proposed DPCD scheme detects social communities under the modest privacy budget. In addition, we show the effectiveness of NAR to perform community detection by querying a limited number of individuals in social networks.
机译：社区检测是在线社交网络中个人之间存在关系的有效方法。在文献中，已经提出了通过利用社交网络的拓扑和社交行为者的属性来开展社区检测的相当少数算法。在实践中，社区检测通常由广告公司和医院等第三方进行，以获得社交网络的不同目的，这很容易导致隐私违规行为。在本文中，我们调查社交网络的社区检测，旨在保护网络拓扑和用户属性的隐私。我们表明，通过额外的先验知识，可以通过查询仅为一小部分而不是整个人群的信息来执行社区检测。特别是，我们首先提出了一种称为差异私有社区检测（DPCD）的新方案。 DPCD通过概率生成模型检测社交网络中的社区，可以将其分解成各个用户解决的子问题。每个用户的私有社交关系和属性由客观扰动保护差别隐私保障。然后，我们提出了一种解析节点隶属恢复（NAR）算法，其也是差异私有的，以通过解决稀疏优化问题来揭示整个群体的社区联盟信息，通过解决稀疏优化问题。通过使用合成和现实世界社交网络的理论分析和实验验证，我们证明拟议的DPCD计划根据温度的隐私预算检测社会社区。此外，我们展示了NAR通过在社交网络中查询有限数量的个人来执行社区检测的有效性。

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