Privacy Preservation For High Dimensional Data Using Slicing Method in Data Mining
In recent days, privacy-preservation for high dimensional data and micro data publishing has seen rapid advances that causes ability to store and record personal data. The method of publishing the data in the web faces many challenges now a days. The data usually contains the individual personal data which are personally identifiable to any person, thus poses the difficulty of Privacy. The existing privacy preserving system has been developed using anonymization techniques of Generalization and Bucketization. Generalization loses the amount of data and at other side Bucketization fail to protect membership disclosure in addition to does not give the clear idea of separation between sensitive attribute as well as quasi-identifying attributes. To solve this problem we introduce novel technique which is useful to partitions data horizontally as well as vertically, which is called as Slicing. Our result shows that slicing useful to preserve better data utility as compare to the Generalization and also protect the membership disclosure. Our contribution on this project is preserves utility as a result of it teams extremely correlate attributes along, and preserves the correlations between such attributes and effective utilization of Hadoop Frame work to handle high dimensional data.