Multidimensional Techniques for Privacy Preservation in Datasets
نویسندگان
چکیده
w w w . i j c s t . c o m InternatIonal Journal of Computer SCIenCe and teChnology 485 Abstract Applications in commercial domains possess large datasets on individuals. This data includes private and sensitive information e.g. patient diseases, bank account details, organization structural details etc. When data mining techniques are applied on these applications the private and sensitive information of the subjects will be revealed. However, it is necessary to share the information in such a way that the identities of the individuals are not revealed. So it is necessary to anonymize the data. For this the quasi attribute set (attribute set that can be linked with original dataset to re-identify individuals) has to identified and anonymized. This paper presents the summary of various anonymization techniques Multidimensional generalization, Multidimensional suppression, Multidimensional Clustering and Multidimensional Cryptography to provide privacy for individuals. Any of these techniques can be applied to achieve privacy for individuals in a better manner.
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تاریخ انتشار 2012