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This work considers active deanonymization of bipartite networks. Thescenario arises naturally in evaluating privacy in various applications such associal networks, mobility networks, and medical databases. For instance, inactive deanonymization of social networks, an anonymous victim is targeted byan attacker (e.g. the victim visits the attacker's website), and the attackerqueries her group memberships (e.g. by querying the browser history) todeanonymize her. In this work, the fundamental limits of privacy, in terms ofthe minimum number of queries necessary for deanonymization, is investigated. Astochastic model is considered, where i) the bipartite network of groupmemberships is generated randomly, ii) the attacker has partial prior knowledgeof the group memberships, and iii) it receives noisy responses to its real-timequeries. The bipartite network is generated based on linear and sublinearpreferential attachment, and the stochastic block model. The victim's identityis chosen randomly based on a distribution modeling the users' risk of beingthe victim (e.g. probability of visiting the website). An attack algorithm isproposed which builds upon techniques from communication with feedback, and itsperformance, in terms of expected number of queries, is analyzed. Simulationresults are provided to verify the theoretical derivations.

Fundamental Privacy Limits in Bipartite Networks under Active Attacks