Privacy-preserving data mining for horizontally-distributed datasets using EGADP

Mohammad Saad Al-Ahmadi*

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review


In this paper, we investigate the possibility of using EGADP for protecting data in horizontallydistributed datasets. EGADP [10] 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.

Original languageEnglish
Number of pages9
StatePublished - 2008

ASJC Scopus subject areas

  • Business and International Management
  • Management of Technology and Innovation


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