Comparisons of randomization and K-degree anonymization schemes for privacy preserving social network publishing

Ying, Xiaowei; Pan, Kai; Wu, Xintao; Guo, Lei

doi:10.1145/1731011.1731021

Cited by 62 publications

(27 citation statements)

References 24 publications

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“…For example, Random Perturbation algorithm [15], Spctr Add/Del [29] and Rand Add/Del-B [30] use this concept to anonymize graphs. Most of k-anonymity methods can be also modelled through Edge add/del concept [34,35,16].…”

Section: Perturbation Methodsmentioning

confidence: 99%

“…Ying et al [30] suggested a variation of Rand Add/Del method, called Blockwise Random Add/Delete strategy or simply Rand Add/Del-B. This method divides the graph into blocks according to the degree sequence and implements modifications (by adding and removing edges) on the nodes at high risk of re-identification, not at random over the entire set of nodes.…”

Section: Anonymizationmentioning

confidence: 99%

“…Ying and Wu [29] and Ying et al [30] used both real space and spectrum based characteristics to study how the graph is affected by randomization methods. The authors focused on four real space characteristics of the graph and on two important eigenvalues of the graph spectrum.…”

Section: Graph Assessmentmentioning

confidence: 99%

“…Transitivity [29,30,6] (T) is the fraction of all possible triangles present in the graph. Possible triangles are identified by the number of triads (two edges with a shared node), as we can see in Equation 7.…”

Section: Graph Assessmentmentioning

confidence: 99%

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Anonymizing graphs: measuring quality for clustering

2014

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Abstract. Anonymization of graph-based data is a problem which has been widely studied last years and several anonymization methods have been developed. Information loss measures have been carried out to evaluate the noise introduced in the anonymized data. Generic information loss measures ignore the intended anonymized data use. When data has to be released to third-parties, and there is no control on what kind of analyses users could do, these measures are the standard ones. In this paper we study different generic information loss measures for graphs comparing such measures to the cluster-specific ones. We want to evaluate whether the generic information loss measures are indicative of the usefulness of the data for subsequent data mining processes.

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Section: Perturbation Methodsmentioning

confidence: 99%

Section: Anonymizationmentioning

confidence: 99%

Section: Graph Assessmentmentioning

confidence: 99%

Section: Graph Assessmentmentioning

confidence: 99%

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Anonymizing graphs: measuring quality for clustering

2014

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“…By performing experimentation on the Enron dataset, they found out that in order to achieve a meaningful level of anonymity for the nodes in the graph, the random perturbation methods need to add and remove too many edges in the graph. Those methods were revisited by Ying et al [2009], in which they compare the randomperturbation method to the method of k-degree anonymity due to Liu and Terzi [2008]. Based on experimentation on two modestly sized datasets (Enron and Polblogs) they arrived at the conclusion that the deterministic approach of k-degree anonymity preserves the graph features better for given levels of anonymity.…”

Section: Anonymization Of Graphs and Social Networkmentioning

confidence: 99%

Social Network Analysis and Mining for Business Applications

Bonchi

Castillo

Gionis

et al. 2011

ACM Trans. Intell. Syst. Technol.

255

117

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Social network analysis has gained significant attention in recent years, largely due to the success of online social networking and media-sharing sites, and the consequent availability of a wealth of social network data. In spite of the growing interest, however, there is little understanding of the potential business applications of mining social networks. While there is a large body of research on different problems and methods for social network mining, there is a gap between the techniques developed by the research community and their deployment in real-world applications. Therefore the potential business impact of these techniques is still largely unexplored.In this article we use a business process classification framework to put the research topics in a business context and provide an overview of what we consider key problems and techniques in social network analysis and mining from the perspective of business applications. In particular, we discuss data acquisition and preparation, trust, expertise, community structure, network dynamics, and information propagation. In each case we present a brief overview of the problem, describe state-of-the art approaches, discuss business application examples, and map each of the topics to a business process classification framework. In addition, we provide insights on prospective business applications, challenges, and future research directions. The main contribution of this article is to provide a state-of-the-art overview of current techniques while providing a critical perspective on business applications of social network analysis and mining.

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