This work considers the fundamental privacy limits under active fingerprinting attacks in power-law bipartite networks. The scenario arises naturally in social network analysis, tracking user mobility in wireless networks, and forensics applications, among others. A stochastic growing network generation model -- called the popularity-based model -- is investigated, where the bipartite network is generated iteratively, and in each iteration vertices attract new edges based on their assigned popularity values. It is shown that using the appropriate choice of initial popularity values, the node degree distribution follows a power-law distribution with arbitrary parameter $\alpha>2$, i.e. fraction of nodes with degree $d$ is proportional to $d^{-\alpha}$. An active fingerprinting deanonymization attack strategy called the augmented information threshold attack strategy (A-ITS) is proposed which uses the attacker's knowledge of the node degree distribution along with the concept of information values for deanonymization. Sufficient conditions for the success of the A-ITS, based on network parameters, are derived. It is shown through simulations that the proposed attack significantly outperforms the state-of-the-art attack strategies.

翻译：这项工作考虑了在积极点印攻击权力法双边网络下的基本隐私限制。 这种情景自然出现在社会网络分析、追踪无线网络用户流动和法证应用中。 调查了一种随机增长的网络生成模型 -- -- 称为以普及为基础的模型 -- -- 即双边网络是迭接生成的,在每个循环脊椎中,根据指定的广度值吸引新的边缘。 事实证明,使用对初选的普及值的适当选择,节点分配遵循一种以任意参数$\alpha>2$(即按美元比例计算的比例的节点与美元)成正比。 一项称为强化信息临界攻击战略(A-ITS)的主动指印匿名攻击战略是使用攻击者对节点分布的知识以及非匿名化信息值的概念。 根据网络参数计算出A-ITS的成功条件。 模拟表明,拟议的攻击大大超越了攻击战略。