Differential Privacy Preserving Data Publishing Based on Bayesian Network

Privacy-preserving data publishing is a hot issue in the field of privacy protection. Differential privacy is a burgeoning technology of privacy protection which provides a powerful privacy mechanism and does not make restrictive assumptions on the attacker's background knowledge. At present, there does not have an effective way to generate Synthetic high-dimensional data with differential privacy technology. Aiming at the issue of high-dimensional privacy data publishing, this paper proposed a method called APrivBayes, which altered the structure of Bayesian network to make it adapting differential privacy mechanism. Then proposed a first node selection mechanism based on attribute correlation degree for the new structure of Bayesian network. Through theoretical analysis and experimental evaluation, this method improves the effect of network and reduces Laplace noise effectively, while protecting personal privacy and improving the usability of published data.