Publishing Community-Preserving Attributed Social Graphs with a Differential Privacy Guarantee

Chen, Xihui; Mauw, Sjouke; Ramírez-Cruz, Yunior

doi:10.2478/popets-2020-0066

Cited by 16 publications

(20 citation statements)

References 58 publications

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“…While these methods do not necessarily view vertex ids as sensitive, data holders whose goal in preventing reidentification attacks is to prevent the adversary from learning the existence of relations may view this approach as an alternative to k-anonymity-based methods. Another DP-based alternative to k-anonymity-based methods consists in learning the parameters of a graph generative model under differential privacy and then using this model to publish synthetic graphs that resemble the original one in some structural properties [10,20,34,40,49,51].…”

Section: Other Privacy Modelsmentioning

confidence: 99%

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

2022

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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.

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Section: Other Privacy Modelsmentioning

confidence: 99%

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

2022

Self Cite

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“…In this section, we outline some techniques and algorithms dedicated to protect the graph datasets. We divide them into four categories: (1) identity and link disclosure [15,57,60], (2) dK-graph generation model [12,48,54], (3) platforms and programming languages [40,45,46], and, finally, (4) static [32,42], and dynamic graphs anonymization [5,6,57].…”

Section: Related Workmentioning

confidence: 99%

“…DP is applied on these parameters to create noisy versions, and, finally, new dereived graphs are generated using a generation model. Chen et al [12] present a method for publishing graphs under DP. They rely on a community-preserving generative model called CAGM.…”

Section: Dk-graph Generation Modelmentioning

confidence: 99%

A User-Centric Mechanism for Sequentially Releasing Graph Datasets under Blowfish Privacy

Chicha

Bouna

Nassar

et al. 2021

ACM Trans. Internet Technol.

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In this article, we present a privacy-preserving technique for user-centric multi-release graphs. Our technique consists of sequentially releasing anonymized versions of these graphs under Blowfish Privacy. To do so, we introduce a graph model that is augmented with a time dimension and sampled at discrete time steps. We show that the direct application of state-of-the-art privacy-preserving Differential Private techniques is weak against background knowledge attacker models. We present different scenarios where randomizing separate releases independently is vulnerable to correlation attacks. Our method is inspired by Differential Privacy (DP) and its extension Blowfish Privacy (BP). To validate it, we show its effectiveness as well as its utility by experimental simulations.

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“…ey proposed LDPGen which incrementally clusters users based on their connections to different partitions of the whole population and adapted existing social graph generation models to construct a synthetic social graph. Chen et al [15] presented a method for publishing private synthetic graphs, which preserves the community structure of the original graph without sacrificing the ability to capture global structural properties. Wang et al [16] presented a differential privacy method for weighted network through structuring a private probability model.…”

Section: Differentially Private Networkmentioning

confidence: 99%

Differentially Private Attributed Network Releasing Based on Early Fusion

Wang

Yang

Zhan

2021

Security and Communication Networks

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Vertex attributes exert huge impacts on the analysis of social networks. Since the attributes are often sensitive, it is necessary to seek effective ways to protect the privacy of graphs with correlated attributes. Prior work has focused mainly on the graph topological structure and the attributes, respectively, and combining them together by defining the relevancy between them. However, these methods need to add noise to them, respectively, and they produce a large number of required noise and reduce the data utility. In this paper, we introduce an approach to release graphs with correlated attributes under differential privacy based on early fusion. We combine the graph topological structure and the attributes together with a private probability model and generate a synthetic network satisfying differential privacy. We conduct extensive experiments to demonstrate that our approach could meet the request of attributed networks and achieve high data utility.

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Publishing Community-Preserving Attributed Social Graphs with a Differential Privacy Guarantee

Cited by 16 publications

References 58 publications

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

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

A User-Centric Mechanism for Sequentially Releasing Graph Datasets under Blowfish Privacy

Differentially Private Attributed Network Releasing Based on Early Fusion

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