In the process of privacy preserving high-dimensional data publishing, the size of the differential privacy budget directly affects the addition of noise. The privacy budget cannot be allocated reasonably for independent low-dimensional attribute sets, compromising the security and restricting availability of composite data sets. Then a Personalized Privacy Budget Allocation (PPBA) algorithm is proposed. The maximum support tree and weight values of attribute nodes are introduced to reduce the candidate space of attribute relationship pairs selected by the differential privacy index mechanism and enhance the accuracy of the Bayesian network. The dynamic weight values of nodes in the Bayesian network are set to rank the sensitivity of low-dimensional attribute sets. According to the personalized requirements for security and availability of published data sets, the constant allocation ratio q of differential privacy budgets is customized for the personalized allocation of Laplace noise to the low-dimensional attribute sets sorted by sensitivity. Theoretical analysis and experimental results reveal that the PPBA algorithm can meet the personalized requirements for security and availability of high-dimensional data publishing, with lower time complexity.