Differential Privacy Preserving Algorithm for Data Anonymization

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Data mining methodology can help associating knowledge gaps in human understanding. Such as analysis of any student dataset gives a better student model yields better instruction, which leads to improved learning. More accurate skill diagnosis leads to better prediction of what a student knows which provides better assessment. Better assessment leads to more efficient learning overall. The main objectives of data mining in practice tend to be prediction and description [4, 5]. Predicting performance involves variables, IAT marks and assignment grades etc. in the student database to predict the unknown values. Data mining is the core process of knowledge discovery in databases. It is the process of extracting of useful patterns from the large database. In order to analyze large amount of information, the area of Knowledge Discovery in Databases (KDD) provides techniques by which the interesting patterns are extracted. Therefore, KDD utilizes methods at the cross point of machine learning, statistics and database systems.

As the dataset obtained from the above steps contain many unnecessary information which one need to be removed for making proper operation on those sets. This can be understood as let the name be the same as it is in the original set so to put this column in the original dataset is not necessary and it can be removed move from the above set of vectors, while if to hide information of the salary of the individual then one has to make changes from the original, therefore this kind of numeric data which need to be hide is perturbed by our method.

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