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An Arithmetic Differential Privacy Budget Allocation Method for the Partitioning and Publishing of Location Information

机译：用于分区和发布位置信息的算术差异隐私预算分配方法

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摘要

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 e-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.
机译：移动互联网服务的快速发展和智能终端的广泛应用加速了大数据的有前景的出现。基于位置信息的许多大数据服务为用户带来了便利，但是，它也会导致个人隐私的严重泄漏。与差分隐私模型相结合的分区和发布方法可以提供更好的范围，以确保用户位置隐私的前提下的查询结果。然而，大多数现有的研究研究只关注位置大数据的分区过程中的结构设计，忽略差异隐私预算分配方法对公布结果的影响。因此，本文提出了一种有效的算术隐私预算分配策略，用于基于树的划分和发布的位置大数据，其满足电子差别隐私。对大量实际数据集的实验结果证明，与提高公布数据的可用性的现有方法相比，提出的隐私预算分配方法较高。

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