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Computer Science > Machine Learning

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[Submitted on 6 Feb 2024]

Title:On provable privacy vulnerabilities of graph representations

Authors:Ruofan Wu, Guanhua Fang, Qiying Pan, Mingyang Zhang, Tengfei Liu, Weiqiang Wang, Wenbiao Zhao
Download a PDF of the paper titled On provable privacy vulnerabilities of graph representations, by Ruofan Wu and 6 other authors
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Abstract:Graph representation learning (GRL) is critical for extracting insights from complex network structures, but it also raises security concerns due to potential privacy vulnerabilities in these representations. This paper investigates the structural vulnerabilities in graph neural models where sensitive topological information can be inferred through edge reconstruction attacks. Our research primarily addresses the theoretical underpinnings of cosine-similarity-based edge reconstruction attacks (COSERA), providing theoretical and empirical evidence that such attacks can perfectly reconstruct sparse Erdos Renyi graphs with independent random features as graph size increases. Conversely, we establish that sparsity is a critical factor for COSERA's effectiveness, as demonstrated through analysis and experiments on stochastic block models. Finally, we explore the resilience of (provably) private graph representations produced via noisy aggregation (NAG) mechanism against COSERA. We empirically delineate instances wherein COSERA demonstrates both efficacy and deficiency in its capacity to function as an instrument for elucidating the trade-off between privacy and utility.
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:2402.04033 [cs.LG]
  (or arXiv:2402.04033v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2402.04033
arXiv-issued DOI via DataCite

Submission history

From: Ruofan Wu [view email]
[v1] Tue, 6 Feb 2024 14:26:22 UTC (17,745 KB)
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