Privacy-Preserving Data Mining for Horizontally-Distributed Datasets using EGADP

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Provided by: IBIMA Publishing
Topic: Big Data
Format: PDF
In this paper, the authors investigate the possibility of using EGADP for protecting data in horizontally distributed datasets. EGADP is a new advanced data perturbation method that masks confidential numeric attributes in original datasets while reproducing all linear relationships in masked datasets. It is developed for centralized datasets that are owned by one owner, and no study (to the best of their knowledge) suggests and investigates empirically the possibilities of using it to protect distributed confidential datasets. This paper is intended to fill this gap.
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