Abstract
In this paper, we 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 our knowledge, suggests and investigates empirically the possibilities of using it to protect distributed confidential datasets. This study is intended to fill this gap.