时变多层社会图的k-化
On the k-Anonymization of Time-varying and Multi-layer Social Graphs
摘要：
在线社交媒体平台的普及为研究现实世界的复杂互动网络提供了前所未有的机会。然而，向研究人员和公众发布这些数据的代价是可能暴露隐私和敏感的用户信息。已经表明，通过移除节点的身份来对网络进行朴素的匿名化不足以保护用户的隐私。为了应对恶意攻击，已经提出了k-匿名解决方案来部分混淆可用于推断节点身份的拓扑信息。在本文中，我们研究了在时变图中确保k-匿名的问题，即，具有随时间变化的结构的图，以及多层图，即，具有多种类型链接的图表。更具体地说，我们研究的情况下，攻击者可以访问的程度的节点。目标是生成一个新的图，其中给定图的每个（时间）层中的节点的度，这样的节点与图中的其他k-1个节点保持不可区分。为了实现这一点，我们找到了图节点的最佳划分，使得每个组内的匿名化度信息的成本最小。我们表明，这减少到一个特殊的情况下，广义分配问题，我们提出了一个简单而有效的算法来解决它。最后，我们引入了一个迭代线性规划方法，以加强匿名度序列的可实现性。该方法的有效性进行评估，通过一组广泛的实验合成和现实世界的图形。
英文摘要：
The popularity of online social media platforms provides an unprecedented opportunity to study real-world complex networks of interactions. However, releasing this data to researchers and the public comes at the cost of potentially exposing private and sensitive user information. It has been shown that a naive anonymization of a network by removing the identity of the nodes is not sufficient to preserve users' privacy. In order to deal with malicious attacks, k-anonymity solutions have been proposed to partially obfuscate topological information that can be used to infer nodes' identity. In this paper, we study the problem of ensuring k-anonymity in time-varying graphs, i.e., graphs with a structure that changes over time, and multi-layer graphs, i.e., graphs with multiple types of links. More specifically, we examine the case in which the attacker has access to the degree of the nodes. The goal is to generate a new graph where, given the degree of a node in each (temporal) layer of the graph, such a node remains indistinguishable from other k-1 nodes in the graph. In order to achieve this, we find the optimal partitioning of the graph nodes such that the cost of anonymizing the degree information within each group is minimum. We show that this reduces to a special case of a Generalized Assignment Problem, and we propose a simple yet effective algorithm to solve it. Finally, we introduce an iterated linear programming approach to enforce the realizability of the anonymized degree sequences. The efficacy of the method is assessed through an extensive set of experiments on synthetic and real-world graphs.
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