2013
DOI: 10.1109/tkde.2011.232
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Anonymization of Centralized and Distributed Social Networks by Sequential Clustering

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Cited by 99 publications
(51 citation statements)
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“…This vertical partitioning can be developed using the following example the are some data sets from hospital and then some data sets from super market but there is a relation from people verification from two data sets [6,9]. By taking good association rule mining in their prescribed data, for example we will find a rule {beef meat, sugar} =>{Diabetes} that means most people who consume beef, meat, sugar suffer diabetes in this case we have vertical partitioned data Because each site's dataset is different with others, but they have a relational field that join their data together.…”
Section: Secure Association Rule Mining Over Vertical Data Partitionementioning
confidence: 99%
“…This vertical partitioning can be developed using the following example the are some data sets from hospital and then some data sets from super market but there is a relation from people verification from two data sets [6,9]. By taking good association rule mining in their prescribed data, for example we will find a rule {beef meat, sugar} =>{Diabetes} that means most people who consume beef, meat, sugar suffer diabetes in this case we have vertical partitioned data Because each site's dataset is different with others, but they have a relational field that join their data together.…”
Section: Secure Association Rule Mining Over Vertical Data Partitionementioning
confidence: 99%
“…Tassa et al [56] The first study of privacy preservation in distributed social networks which s shown to outperform SaNGreeA algorithm which is the leading algorithm for achieving anonymity in networks by means of clustering 2013…”
Section: Validated On Facebook For 700 Users 2010mentioning
confidence: 99%
“…[59]. However, in case of social network distributed privacy preserving techniques are not well reported in literature except [56].…”
Section: Research Directionsmentioning
confidence: 99%
“…Here we apply anonymization process to user personal data with the support of anonymity analyzer in the SPMW. The anonymization process in this module is very flexible and is done in different levels as [16,17] and supports diverse implementation techniques such as [16,18,19,20]. The anonymous data are basically generalized in three different levels according to the user request and feedback regarding generalization level.…”
Section: Anonymizermentioning
confidence: 99%
“…Afterwards, a copy of these data should be sent to the service provider based on privacy level. To anonymize data different algorithms may be applied [16,18,19,20]. The algorithm blends the anonymous data got in previous steps with other users' anonymous data available in the service provider side to make the user data tracking impossible.…”
Section: H Anonymous Feedbackmentioning
confidence: 99%