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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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