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See detailPreventing active re-identification attacks on social graphs via sybil subgraph obfuscation
Mauw, Sjouke UL; Ramirez Cruz, Yunior UL; Trujillo Rasua, Rolando UL

in Knowledge and Information Systems (2022), 64

Active re-identification attacks constitute a serious threat to privacy-preserving social graph publication, because of the ability of active adversaries to leverage fake accounts, a.k.a. sybil nodes, to ... [more ▼]

Active re-identification attacks constitute a serious threat to privacy-preserving social graph publication, because of the ability of active adversaries to leverage fake accounts, a.k.a. sybil nodes, to enforce structural patterns that can be used to re-identify their victims on anonymised graphs. Several formal privacy properties have been enunciated with the purpose of characterising the resistance of a graph against active attacks. However, anonymisation methods devised on the basis of these properties have so far been able to address only restricted special cases, where the adversaries are assumed to leverage a very small number of sybil nodes. In this paper, we present a new probabilistic interpretation of active re-identification attacks on social graphs. Unlike the aforementioned privacy properties, which model the protection from active adversaries as the task of making victim nodes indistinguishable in terms of their fingerprints with respect to all potential attackers, our new formulation introduces a more complete view, where the attack is countered by jointly preventing the attacker from retrieving the set of sybil nodes, and from using these sybil nodes for re-identifying the victims. Under the new formulation, we show that k-symmetry, a privacy property introduced in the context of passive attacks, provides a sufficient condition for the protection against active re-identification attacks leveraging an arbitrary number of sybil nodes. Moreover, we show that the algorithm K-Match, originally devised for efficiently enforcing the related notion of k-automorphism, also guarantees k-symmetry. Empirical results on real-life and synthetic graphs demonstrate that our formulation allows, for the first time, to publish anonymised social graphs (with formal privacy guarantees) that effectively resist the strongest active re-identification attack reported in the literature, even when it leverages a large number of sybil nodes. [less ▲]

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See detailActive Re-identification Attacks on Periodically Released Dynamic Social Graphs
Chen, Xihui UL; Kepuska, Ema UL; Mauw, Sjouke UL et al

in Chen, Liqun; Li, Ninghui; Liang, Kaitai (Eds.) et al Computer Security - ESORICS 2020 (2020, September 13)

Active re-identification attacks pose a serious threat to privacy-preserving social graph publication. Active attackers create fake accounts to enforce structural patterns that can be used to re-identify ... [more ▼]

Active re-identification attacks pose a serious threat to privacy-preserving social graph publication. Active attackers create fake accounts to enforce structural patterns that can be used to re-identify legitimate users on published anonymised graphs, even without additional background knowledge. So far, this type of attacks has only been studied in the scenario where the inherently dynamic social graph is published once. In this paper, we present the first active re-identification attack in the more realistic scenario where a dynamic social graph is periodically published. Our new attack leverages tempo-structural patterns, created by a dynamic set of sybil nodes, for strengthening the adversary. We evaluate our new attack through a comprehensive set of experiments on real-life and synthetic dynamic social graphs. We show that our new attack substantially outperforms the most effective static active attack in the literature by increasing success probability by at least two times and efficiency by at least 11 times. Moreover, we show that, unlike the static attack, our new attack remains at the same level of efficiency as the publication process advances. Additionally, we conduct a study on the factors that may thwart our new attack, which can help design dynamic graph anonymisation methods displaying a better balance between privacy and utility. [less ▲]

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See detailPublishing Community-Preserving Attributed Social Graphs with a Differential Privacy Guarantee
Chen, Xihui UL; Mauw, Sjouke UL; Ramirez Cruz, Yunior UL

in Proceedings on Privacy Enhancing Technologies (2020), 2020(4), 131-152

We present a novel method for publishing differentially private synthetic attributed graphs. Our method allows, for the first time, to publish synthetic graphs simultaneously preserving structural ... [more ▼]

We present a novel method for publishing differentially private synthetic attributed graphs. Our method allows, for the first time, to publish synthetic graphs simultaneously preserving structural properties, user attributes and the community structure of the original graph. Our proposal relies on CAGM, a new community-preserving generative model for attributed graphs. We equip CAGM with efficient methods for attributed graph sampling and parameter estimation. For the latter, we introduce differentially private computation methods, which allow us to release communitypreserving synthetic attributed social graphs with a strong formal privacy guarantee. Through comprehensive experiments, we show that our new model outperforms its most relevant counterparts in synthesising differentially private attributed social graphs that preserve the community structure of the original graph, as well as degree sequences and clustering coefficients. [less ▲]

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See detailDistance-based vertex identification in graphs: The outer multiset dimension
Gil-Pons, Reynaldo; Ramirez Cruz, Yunior UL; Trujillo-Rasua, Rolando et al

in Applied Mathematics and Computation (2019), 363

Detailed reference viewed: 100 (5 UL)
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See detailRobust active attacks on social graphs
Mauw, Sjouke UL; Ramirez Cruz, Yunior UL; Trujillo Rasua, Rolando UL

in Data Mining and Knowledge Discovery (2019), 33(5), 1357-1392

Detailed reference viewed: 114 (7 UL)
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See detailSemi-automatically Augmenting Attack Trees using an Annotated Attack Tree Library
Jhawar, Ravi UL; Lounis, Karim UL; Mauw, Sjouke UL et al

in Katsikas, Sokratis; Alcaraz, Cristina (Eds.) Security and Trust Management. STM 2018. (2018, October)

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See detailAnonymising social graphs in the presence of active attackers
Mauw, Sjouke UL; Ramirez Cruz, Yunior UL; Trujillo Rasua, Rolando UL

in Transactions on Data Privacy (2018), 11(2), 169-198

Detailed reference viewed: 78 (4 UL)
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See detailConditional adjacency anonymity in social graphs under active attacks
Mauw, Sjouke UL; Ramirez Cruz, Yunior UL; Trujillo Rasua, Rolando UL

in Knowledge and Information Systems (2018)

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See detailThe Local Metric Dimension of the Lexicographic Product of Graphs
Barragán-Ramírez, Gabriel A; Estrada-Moreno, Alejandro; Ramirez Cruz, Yunior UL et al

in Bulletin of the Malaysian Mathematical Sciences Society (2018)

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See detailThe Simultaneous Local Metric Dimension of Graph Families
Barragan-Ramirez, Gabriel; Estrada-Moreno, Alejandro; Ramirez Cruz, Yunior UL et al

in Symmetry (2017), 9(8),

Detailed reference viewed: 122 (4 UL)