There has been much research over the last decades on methods for limiting disclosure in data publishing; in particular, the computer science community has made important contributions over the last twenty years. Recent research studied the problem of publishing microdata or network data without revealing sensitive information, leading to the privacy preserving paradigms. Organization would like to publish their data for researching, advertising or maybe for prediction. K-anonymity, l-diversity, generalization, clustering and randomization are among the frequent techniques that are be used in anonymization process. However these existing techniques suffer from at least one of the following drawbacks: (i) The information loss and (ii) loss of the privacy data.
The novelty of this thesis is to produce the algorithm that can be implement to do the anonymize process into network data. With enhance the current technique, this project produce a solution to anonymize data especially the network data. Currently the process of anonymize the data occur TWO main problem; there are information loss and privacy of data. The researcher believe the appropriate technique will produce the solution and here we come with the technique and the algorithm which we believe will solve the problem
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