Privacy Preservation by k-Anonymization of Weighted Social Networks

Skarkala, Maria Eleni; Μaragoudakis, Manolis; Gritzalis, Stefanos; Mitrou, Lilian; Toivonen, Hannu; Moen, Pirjo

doi:10.1109/asonam.2012.75

Cited by 33 publications

(16 citation statements)

References 18 publications

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“…In this section, we report a systematic empirical study to evaluate the RPVS algorithm, compare our approach with the KM, KH, KA and KN algorithms techniques (KM: Kautomorphism publication in [16], KH: K-Histogram anonymization in [17], KA: K-Anonymous Algorithm in [18], KN: K-anonymity against 1-neighborhood attacks in [5]) and validate our analysis using real social network datasets. All of the algorithms were implemented in C++, and the experiments were executed on a CPU machine with 3.4GHz Intel Core i3 Dual processor and 4GB of RAM, running Windows 7.…”

Section: Resultsmentioning

confidence: 98%

Preserving weighted social networks privacy using vectors similarity

Lan

2015

2015 8th International Conference on Biomedical Engineering and Informatics (BMEI)

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Aiming at weighted social networks, a random perturbation method based on vectors similarity is proposed. It can protect structures and edge weights of weighted social networks in multiple release scenarios. First, it partitions weighted social networks into t sub-graphs by the segmentation method based on vertex cluster using edge space of graph theory, describes these sub-graphs by vectors , and constructs vector set models of weighted social networks. Then, it adopts weighted Euclidean distance as the metrics of vectors similarity to construct the released candidate sets of t sub-graphs according to the threshold designated by publishers. Finally, it randomly selects vectors from the candidate sets to construct the released vector set, and builds the published weighted social networks based on the released vector set. The proposed method can resist multiple vertex recognition attacks, force the attackers to reidentify in a large result set that the existential probabilities of the vectors are same, and increase the uncertainty of recognition. The experimental results on the actual datasets demonstrate that the proposed method can preserve the security of individuals' privacy, meanwhile it can protect some structure characteristics for social networks analysis and improve the released data utility.

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Section: Resultsmentioning

confidence: 98%

Preserving weighted social networks privacy using vectors similarity

Lan

2015

2015 8th International Conference on Biomedical Engineering and Informatics (BMEI)

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“…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. Dataset used: KarateClub, Lesmis Panda et al [17] proposed L-diversity for preserving privacy in collaborative social network data and the effect on the utility of the data for social network analysis has been seen.…”

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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“…Lan et al [26] developed an algorithm called KNAP against 1-neighborhood attack for publishing social networks data. Skarkala et al [27] applied K-anonymity to weighted social networks. Liu et al [28] proposed the concept of k-degree to prevent vertex re-identification through the information of vertex degree.…”

Section: Fig 2 Social Network Graph With 7 Nodes and Sensitive Attrimentioning

confidence: 99%