The rapid development of mobile Internet services and the wide application of intelligent terminals has accelerated the advent of the promising era of big data. A number of big data services based on location information bring convenience to users, however, it also results in serious leakage of personal privacy. The partitioning and publishing method combined with the differential privacy model can provide better range counting query results under the premise of ensuring the privacy of users' location. Nevertheless, most of the existing research studies only focus on the structural design during the partitioning process of location big data and ignore the impact of differential privacy budget allocation methods on the published results. This paper, therefore, proposes an efficient arithmetic privacy budget allocation strategy for the tree-based partitioning and publishing of location big data which satisfies the varepsilon-differential privacy. Experimental results over a large number of real-world datasets prove that the proposed privacy budget allocation method is superior in contrast to the existing methods for improving the usability of the published data.