Preservation of Structural Properties in Anonymized Social Networks

Truta, Traian Marius; Câmpan, Alina; Ralescu, Anca

doi:10.4108/icst.collaboratecom.2012.250414

Cited by 9 publications

(7 citation statements)

References 35 publications

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“…These three initial social networks (Enron, Random, and ScaleFree) were anonymized using FKDA and Sangreea with several values for the anonymity parameter k: 2, 3, 4, 5, 6,7,8,9,10,15,20,25, and 50. The Sangreea networks were deanonymized with R-Mat De-anonymization and Uniform Deanonymization.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…The anonymized networks were not de-anonymized and the comparison was done directly between the original networks and the anonymized networks; due to how anonymization was performed, the anonymized networks had a smaller number of nodes than the original ones, and this could be an unfairness factor in performing the said comparison. In addition to studying the behavior of centrality measures, another utility preserving research work was conducted that compared additional measures like diameter, clustering coefficients, and topological indices [15]. In this paper, the same clustering-based anonymization technique was studied as in [14], but the super-nodes of the anonymized networks were de-anonymized randomly and the comparison were done on the final de-anonymized networks and the original ones; comparison was therefore more fair due to deanonymization.…”

Section: Related Workmentioning

confidence: 99%

“…De-anonymization is implemented based on the information packaged in the aggregated (anonymized) network and assumes certain statistical distribution of the nodes' degrees. The two de-anonymization methods we used are Uniform De-anonymization [15] and R-Mat De-anonymization.…”

Section: Sangreea De-anonymizationmentioning

confidence: 99%

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Preservation of Centrality Measures in Anonymized Social Networks

Alufaisan

Câmpan

2013

2013 International Conference on Social Computing

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Social media sites became a pervasive presence in the nowadays society. We can learn a lot of useful information about human behavior and interaction by paying attention to the information and relations of social media users. This information can be public or private. Protecting the private information of the users in social networks is a real concern. Among other perturbation methods, anonymization models have been used to ensure the privacy of social network users. Each anonymization model has its own assumptions about the information that needs protection and various possible attacks attempting information disclosure. A conflicting goal with maintaining the privacy of a network's information is the preservation of the structural properties of the social network. A good anonymization model would preserve the privacy of social networks' users and preserve enough information to allow a good analysis of the properties of the social networks. In this paper we investigate how well two anonymization methods preserve the importance of nodes in the network, where node importance is expressed by centrality measures. In the same time, we examine the privacy concerns remnant in these anonymized social networks. Our experiments show that there is an inverse correlation between preserving structural properties of social networks and protecting the privacy of their users. The more information is preserved, the weakest the privacy protection. Also, we learned that different anonymity approaches can preserve information better in certain network types.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Preservation of Centrality Measures in Anonymized Social Networks

Alufaisan

Câmpan

2013

2013 International Conference on Social Computing

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“…Dataset used: Hepth, Enron, Net-trace Lan et al [14] An algorithm called KNAP against 1-neighborhood attack has been developed for publishing social networks data. Dataset used: Synthetic data Truta et al [15] Studied how well several structural properties of a social network are preserved through an anonymization process. Dataset used: R-MAT, ScaleFree, Enron, Random1,Random2 Skarkala et al [16] K-anonymity applied to weighted social networks.…”

Section: Table1 Brief Of Anonymization Using K-anonymity Author Briementioning

confidence: 99%

An Approach of Privacy Preserving based Publishing in Twitter

Singh

Bansal

Sofat

2014

Proceedings of the 7th International Conference on Security of Information and Networks

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With the increase in online publishing of social network data the requirement to protect confidential information related to users has become the main concern of publishers. To cater to this need many anonymization techniques like K-anonymity, L-diversity and T-closeness has been proposed by various researchers for micro-data as well as social network data. In this paper we aim to protect sensitive information of users of Twitter-second most popular social networking site. For the purpose of carrying out anonymization, a crawler has been developed to collect data of around 10K users from publicly available information. Data of around 30 users have been used to carry out the experimental work using ARX tool. All three anonymization methods: Kanonymity, L-diversity and T-closeness have been used. Performance of technique is evaluated using information gain as a metric.

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“…These graph invariants play an important role in many scientific areas, notably in chemistry and network theory (see for example [12] and [1,18]). In earlier works [2, 3, 5-7, 9-17, 19], several bounds for M 1 and N M 1 were reported.…”

Section: Introductionmentioning

confidence: 99%