Anonymous social networks present a number of new and challenging problems for existing Social Network Analysis techniques. Traditionally, existing methods for analysing graph structure, such as community detection, required global knowledge of the graph structure. That implies that a centralised entity must be given access to the edge list of each node in the graph. This is impossible for anonymous social networks and other settings where privacy is valued by its participants. In addition, using their graph structure inputs for learning tasks defeats the purpose of anonymity. In this work, we hypothesise that one can re-purpose the use of the HyperANF a.k.a HyperBall algorithm -- intended for approximate diameter estimation -- to the task of privacy-preserving community detection for friend recommending systems that learn from an anonymous representation of the social network graph structure with limited privacy impacts. This is possible because the core data structure maintained by HyperBall is a HyperLogLog with a counter of the number of reachable neighbours from a given node. Exchanging this data structure in future decentralised learning deployments gives away no information about the neighbours of the node and therefore does preserve the privacy of the graph structure.

中文翻译：

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近似保护隐私的邻域估计

匿名社交网络为现有的社交网络分析技术提出了许多新的挑战性问题。传统上，用于分析图结构的现有方法（例如社区检测）需要对图结构有全局了解。这意味着必须授予集中式实体访问图中每个节点的边缘列表的权限。对于匿名社交网络和其他由参与者重视隐私的环境而言，这是不可能的。此外，将其图结构输入用于学习任务会破坏匿名性的目的。在这项工作中，我们假设可以重新使用HyperANF ak 一种HyperBall算法-用于近似直径估计-适用于朋友推荐系统的保护隐私的社区检测任务，该推荐系统从对社交网络图结构的匿名表示中学习而受到的隐私影响有限。这是可能的，因为HyperBall维护的核心数据结构是HyperLogLog，其计数器具有给定节点可到达的邻居数。在未来的分散式学习部署中交换此数据结构不会丢失有关节点邻居的任何信息，因此确实保留了图结构的私密性。这是可能的，因为HyperBall维护的核心数据结构是HyperLogLog，其计数器具有给定节点可到达的邻居数。在未来的分散式学习部署中交换此数据结构不会丢失有关节点邻居的任何信息，因此确实保留了图结构的私密性。这是可能的，因为HyperBall维护的核心数据结构是HyperLogLog，其计数器具有给定节点可到达的邻居数。在未来的分散式学习部署中交换此数据结构不会丢失有关节点邻居的任何信息，因此确实保留了图结构的私密性。