How to Hide One’s Relationships from Link Prediction Algorithms

Abstract

Our private connections can be exposed by link prediction algorithms. To date, this threat has only been addressed from the perspective of a central authority, completely neglecting the possibility that members of the social network can themselves mitigate such threats. We fill this gap by studying how an individual can rewire her own network neighborhood to hide her sensitive relationships. We prove that the optimization problem faced by such an individual is NP-complete, meaning that any attempt to identify an optimal way to hide one’s relationships is futile. Based on this, we shift our attention towards developing effective, albeit not optimal, heuristics that are readily-applicable by users of existing social media platforms to conceal any connections they deem sensitive. Our empirical evaluation reveals that it is more beneficial to focus on “unfriending” carefully-chosen individuals rather than befriending new ones. In fact, by avoiding communication with just 5 individuals, it is possible for one to hide some of her relationships in a massive, real-life telecommunication network, consisting of 829,725 phone calls between 248,763 individuals. Our analysis also shows that link prediction algorithms are more susceptible to manipulation in smaller and denser networks. Evaluating the error vs. attack tolerance of link prediction algorithms reveals that rewiring connections randomly may end up exposing one’s sensitive relationships, highlighting the importance of the strategic aspect. In an age where personal relationships continue to leave digital traces, our results empower the general public to proactively protect their private relationships.

Social Diffusion Sources Can Escape Detection

“…Such hiding techniques can be used to prevent a closely-cooperating group of nodes from being identified by community detection algorithms [41], and prevent a leader of the organization from being recognized by centrality measures in both standard [40,43] and multilayer networks [39]. Similar techniques can be used to prevent an undisclosed relationship from being pinpointed by link prediction algorithms [42,46]. Nevertheless, none of the existing works considered strategically hiding the source of diffusion from source detection algorithms.…”

Hiding in Temporal Networks

“…characterization of centrality measures that are resilient to being fooled [30], analyze the possible strategies of an adversary who is aware of the existence of nodes that want to hide themselves [31], or consider the problem of hiding from centrality measures in multilayer networks [32]. Other hiding problems considered in the literature include preventing the identification of closely-cooperating groups of nodes by community detection algorithms [27], avoiding the detection of private relationships by link prediction algorithms [33], [34], manipulating the node similarity measures [35], and investigating the possibility of concealing the source of network diffusion from source detection algorithms [36]. All of these works consider only static networks.…”

Adversarial Robustness of Probabilistic Network Embedding for Link Prediction

“…Early studies on robustness for link-level tasks usually target traditional link prediction approaches. That includes link prediction attacks that aim to solve specific problems in the social context, e.g., to hide relationships [10,43] or to disguise communities [42], and works that restrict the perturbation type to only adding or only deleting edges [54,53,48], which could result in less efficient attacks or defenses. The robustness for NE based link prediction is much less investigated than classification, and is considered more often as a way to evaluate the robustness of the NE method, such as in [34,2,38].…”