Robust active attacks on social graphs

Abstract

In order to prevent the disclosure of privacy-sensitive data, such as names and relations between users, social network graphs have to be anonymised before publication. Naive anonymisation of social network graphs often consists in deleting all identifying information of the users, while maintaining the original graph structure. Various types of attacks on naively anonymised graphs have been developed. Active attacks form a special type of such privacy attacks, in which the adversary enrols a number of fake users, often called sybils, to the social network, allowing the adversary to create unique structural patterns later used to re-identify the sybil nodes and other users after anonymisation. Several studies have shown that adding a small amount of noise to the published graph already suffices to mitigate such active attacks. Consequently, active attacks have been dubbed a negligible threat to privacy-preserving social graph publication. In this paper, we argue that these studies unveil shortcomings of specific attacks, rather than inherent problems of active attacks as a general strategy. In order to support this claim, we develop the notion of a robust active attack, which is an active attack that is resilient to small perturbations of the social network graph. We formulate the design of robust active attacks as an optimisation problem and we give definitions of robustness for different stages of the active attack strategy. Moreover, we introduce various heuristics to achieve these notions of robustness and experimentally show that the new robust attacks are considerably more resilient than the original ones, while remaining at the same level of feasibility.

Preventing active re-identification attacks on social graphs via sybil subgraph obfuscation

“…-We prove that the algorithm K-Match, devised to ensure a sufficient condition for k-automorphism, also guarantees k-symmetry. -We provide empirical evidence on the effectiveness of K-Match as an anonymisation strategy against the strongest active attack reported in the literature, namely the robust active attack presented in [32], even when it leverages a large number of sybil nodes.…”

“…5 that the algorithm K-Match efficiently provides a sufficient condition for ksymmetry. Finally, we empirically demonstrate the effectiveness of K-Match against the robust active attack from [32] in Sect. 6 and give our conclusions in Sect.…”

“…Backstrom et al were the first to show the impact of active attacks in social networks back in 2007 [2]. Their attack has been optimised a number of times, see [32,37,38], and two privacy models particularly tailored to measure the resistance of social graphs to this type of attack have been recently proposed [31,46]. The first of those models is (k, )-anonymity, introduced in 2016 by Trujillo-Rasua and Yero [46].…”

“…the number of friends or the relations in the immediate vicinity [35]. Many privacy attacks that solely rely on the underlying graph topology of the social graph exist [1], and they are still effective [32], despite advances on social graph anonymisation. A particularly effective privacy attack is the so-called active attack, which uses a strategy consisting in inserting fake accounts, commonly referred to as sybils, into the real network.…”

Divide-and-Learn: A Random Indexing Approach to Attribute Inference Attacks in Online Social Networks

“…These are motivated by the observed interaction and homophily between network structure and node attributes. An active attack on privacy-preserving publication of social graphs is presented in [19]. Demographic variable attacks on Twitter users based on whom they follow are presented in [4].…”