k-Anonymization Using Multidimensional Suppression for Data De-Identification

As searching methods have advanced the increased risk of privacy disclosure makes it important to protect privacy of user during data publishing. Many of the algorithms used for the data de-identification are not efficient because resulted dataset can easily linked with the public database and it reveals the users identity. One of the method uses for protecting the privacy of user is to apply anonymization algorithms. TDS and TDR using generalization of method to anonymized the dataset. Major drawback as these algorithm is they requires a manually generated domain hierarchy taxonomy for every quasi-identifier in the data set on which k-anonymity has to be performed.

Provided by: International Journal of Computer Applications Topic: Data Management Date Added: Dec 2012 Format: PDF

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