  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.
