Email this article High Data Utility by Employing the Large Scale Data Sampling and Length Constraint Strategy
Abstract
We intend an original differential private frequent item sets mining algorithm for big data by integration the ideas which has improved presentation due to the new example and improved truncation performance. We construct our algorithm on FP-Tree for recurrent item sets mining. In arrange to resolve the trouble of structure FP-Tree with large-scale data; we initial utilize the sampling idea to get hold of delegate data to colliery probable congested recurrent item sets, which are presently used to come across the last everyday items in the large-scale data. In count, we occupy the length constriction policy to get to the bottom of the predicament of elevated global compassion. In particular, we use sequence corresponding thoughts to realize the most an alogous string in the source dataset, and put into operation transaction truncation for attain the lowest information defeat. We lastly add the Laplace noise for frequent item sets to make certain privacy guarantees.
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