The Impact of Unlinkability on Adversarial Community Detection: Effects and Countermeasures

Abstract

Abstract. We consider the threat model of a mobile-adversary drawn from contemporary computer security literature, and explore the dynamics of community detection and hiding in this setting. Using a real-world social network, we examine the extent of network topology information an adversary is required to gather in order to accurately ascertain community membership information. We show that selective surveillance strategies can improve the adversary's efficiency over random wiretapping. We then consider possible privacy preserving defenses; using anonymous communications helps, but not much; however, the use of counter-surveillance techniques can significantly reduce the adversary's ability to learn community membership. Our analysis shows that even when using anonymous communications an adversary placing a selectively chosen 8% of the nodes of this network under surveillance (using key-logger probes) can de-anonymize the community membership of as much as 50% of the network. Uncovering all community information with targeted selection requires probing as much as 75% of the network. Finally, we show that a privacy conscious community can substantially disrupt community detection using only local knowledge even while facing up to the asymmetry of a completely knowledgeable mobile-adversary.

Certified Robustness of Community Detection against Adversarial Structural Perturbation via Randomized Smoothing

“…Multiple studies [8,9,13,28,36] have shown that community detection is vulnerable to adversarial structural perturbation. Several heuristic defenses [9,28] were proposed to enhance the robustness of community detection against adversarial structural perturbations. However, these defenses lack formal guarantees.…”

“…However, to the best of our knowledge, there are no studies to certify robustness of community detection against such adversarial structural perturbation. We note that several heuristic defenses [9,28] were proposed to enhance the robustness of community detection against structural perturbation. However, these defenses lack formal guarantees and can often be defeated by strategic attacks that adapt to them.…”

Privacy of Community Pseudonyms in Wireless Peer-to-Peer Networks

“…Previous work [14] obtained efficient community detection algorithms on graphs. In our setting, we use adversarial community detection [35] algorithms as standard community detection algorithms may fail because of the use of community pseudonyms. After A infers communities, it must guess the relation between inferred and real communities relying on background information on communities profile.…”

Intelligent deception techniques against adversarial attack on the industrial system

“…Some users want to hide information only for fun and some users have some wrong intention behind that deception. The concept of community hiding mechanism was introduced by Nagaraja 31 . He studied the effort of the attacker in breaking the community association of the users via network topology information 7 and traffic flow analysis 36 .…”

“…Multiple CDAs are available to analyze the network structure but, a little work has been done to conceal the community information of the nodes in the network. 10,[31][32][33][34] Researchers found the motivation behind the hiding of information and discussed various deception mechanisms in the social network. Caspi and Gorsky 35 performed a web-based survey on the motivation and emotions for Israeli users' online deception.…”