Resisting structural re-identification in anonymized social networks

Hay, Michael; Miklau, Gerome; Jensen, David; Towsley, Don; Weis, Philipp

doi:10.14778/1453856.1453873

Cited by 436 publications

(267 citation statements)

References 24 publications

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“…Hay et al [9] explore the potential of structural queries on graphs for the reidentification of vertices and propose a formalization of the graph anonymization problem based on k-anonymity. Given a graph G = (V, E) and an anonymization of it, G ′ = (V ′ , E ′ ), they let an adversary post queries on the structure of G in a neigborhood of a fixed vertex v ∈ V ′ .…”

Section: K-anonymous Graphs In Terms Of Structural Queriesmentioning

confidence: 99%

“…The anonymization method proposed in [9] is based on the idea of k-anonymity as a partition of the record set. An algorithm is described that uses simulated annealing in order to find a partition of the vertex set that satisfies the kanonymity constraint and maximizes the descriptive properties of the relations between the classes of the partition.…”

Section: K-anonymous Graphs In Terms Of Structural Queriesmentioning

confidence: 99%

“…The approach in [9] does not fix a unique quasi-identifier, but leaves it up to the data owner to choose which structural attributes are important to publish and/or protect. Among the previous work we have found, it is also probably the approach that is closest to the one presented in this article.…”

Section: K-anonymous Graphs In Terms Of Structural Queriesmentioning

confidence: 99%

“…A clustered graph can be published as a generalized graph as described in [9]. When the graph is k-anonymous, it is possible to publish a generalization of the graph that satisfies k-anonymity, without any information loss at all.…”

Section: A K-anonymization Algorithmmentioning

confidence: 99%

“…At present, due to the interest of online social networks, several authors have studied data protection for graphs. It is relevant here to consider the works in [8,9,13,23], in which alternative definitions of k-anonymity for graphs have been presented. In this work we discuss these different definitions and we show that the definition in [8] have severe weaknesses.…”

Section: Introductionmentioning

confidence: 99%

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Reidentification and k-anonymity: a model for disclosure risk in graphs

Stokes

Torra

2012

Soft Comput

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In this article we provide a formal framework for reidentification in general. We define n-confusion as a concept for modelling the anonymity of a database table and we prove that n-confusion is a generalization of kanonymity. After a short survey on the different available definitions of kanonymity for graphs we provide a new definition for k-anonymous graph, which we consider to be the correct definition. We provide a description of the k-anonymous graphs, both for the regular and the non-regular case. We also introduce the more flexible concept of (k, l)-anonymous graph. Our definition of (k, l)-anonymous graph is meant to replace a previous definition of (k, l)-anonymous graph, which we here prove to have severe weaknesses. Finally we provide a set of algorithms for k-anonymization of graphs.

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Section: K-anonymous Graphs In Terms Of Structural Queriesmentioning

confidence: 99%

Section: K-anonymous Graphs In Terms Of Structural Queriesmentioning

confidence: 99%

Section: K-anonymous Graphs In Terms Of Structural Queriesmentioning

confidence: 99%

Section: A K-anonymization Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Reidentification and k-anonymity: a model for disclosure risk in graphs

Stokes

Torra

2012

Soft Comput

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Efficient subgraph search on large anonymized graphs

Ding

Jia

et al. 2018

Concurrency and Computation

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Summary Graph is one of the most important data structures to model social networks and becomes popular to find interesting relationships between individuals. Since graphs may contain sensitive information, data curators usually need to anonymize the graph before publication to prevent individual re‐identification, which thus leads to plenty of anonymized graphs for data sharing and exploration. However, the new structures and properties of anonymized graphs make the traditional graph indexing method inefficient or even invalid for query processing. To address the subgraph query problem over anonymized graph database, in this paper, we first introduce basic concepts about anonymized graphs and subgraph queries, then propose an index structure named Closure+‐tree to process the subgraph query efficiently. In particular, graphs were organized hierarchically that each node is an union of its child nodes under some specified mapping functions. During the processing of subgraph queries, the whole graph descendants will be pruned if their union does not contain the query graph. To evaluate the performance of our proposed Closure+‐tree, extensive experiments are performed on both real and synthetic graph data sets. The experimental results revealed that our index structure can prune up to 80% unqualified graphs with variable size of queries. Furthermore, the size of our index structure is only around a quarter of the entire anonymized graph data set, which indicates good scalability over large data sets.

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Social network data analysis and mining applications for the Internet of Data

Cuomo

Maiorano

2018

Concurrency and Computation

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Summary Social network analysis is an interdisciplinary topic attracting researchers from biology, economics, psychology, and machine learning, with an existing long history based on graph theory. It has since attracted interests from both the research and business communities for a strong potential and variety of applications. In addition, this interest has been fueled by the large success of online social networking sites and the subsequent abundance of social network data produced. An important aspect in this research field is influence maximization in social networks. The goal is to find a set of individuals to be targeted with the aim to drive social contagion and generate a diffusion cascade. We provide here an overview of the models and approaches used to analyze social networks. In this context, we also discuss data preparation and privacy concerns. We further describe different kind of approaches based on centrality measures, which express a sociological interpretation of the data, and stochastic influence and information propagation techniques, which aim at modeling the underlying diffusion processes that govern social interactions.

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Resisting structural re-identification in anonymized social networks

Cited by 436 publications

References 24 publications

Reidentification and k-anonymity: a model for disclosure risk in graphs

Reidentification and k-anonymity: a model for disclosure risk in graphs

Efficient subgraph search on large anonymized graphs

Social network data analysis and mining applications for the Internet of Data

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