MAXGDDP:decision data release with differential privacy - Jibin FU; Xiaojian ZHANG; Liping DING (Tongxin xuebao)
Specialization-based private decision data release has attracted considerable research attention in recent years.The relation among hierarchical node,taxonomy tree,and budget allocation directly constrains the accuracy of data release and classification.Most existing methods based on hierarchical specialization cannot efficiently address the above problems.An effective method was proposed,called MAXGDDP to publish decision data with specialization.MAXGDDP employed MAX index attribute selection algorithm to select the highlight concept for furthering specialization in each hierarchy.Besides,for making more rational use of privacy budget,MAXGDDP relied on geometric strategy to allocate the privacy budget in each hierarchy.Compared with existing methods such as DiffGen on the real datasets,MAXGDDP outperforms its competitors,achieves data privacy and the better result of classification simultaneously.