Spatial databases are essential to applications in a wide variety of domains. One of the main privacy concerns when answering statistical queries, such as range counting queries, over a spatial database is that an adversary observing changes in query answers may be able to determine whether or not a particular geometric object is present in the database. Differential privacy addresses this concern by guaranteeing that the presence or absence of a geometric object has little effect on query answers. Most of the current differentially private mechanisms for spatial databases ignore the fact that privacy is personal and, thus, provide the same privacy protection for all geometric objects. However, some particular geometric objects may be more sensitive to privacy issues than others, requiring stronger differential privacy guarantees. In this paper, we introduce the concept of spatial personalized differential privacy for spatial databases where different geometric objects have different privacy protection requirements. Also, we present SPDP-PCE, a novel spatial personalized differentially private mechanism to answer range counting queries over spatial databases that fully considers the privacy protection requirements of geometric objects in the underlying geometric space in both steps of noise addition and consistency enforcement. Our experimental results on real datasets demonstrate the effectiveness of SPDP-PCE under various total privacy budgets, query shapes, and privacy level distributions.

空间数据库对于广泛领域中的应用程序至关重要。在空间数据库上回答统计查询（例如范围计数查询）时，主要的隐私问题之一是观察到查询答案变化的对手可能能够确定数据库中是否存在特定的几何对象。差异隐私通过保证几何对象的存在或缺失对查询答案的影响很小来解决此问题。当前，空间数据库的大多数差异私有机制都忽略了隐私是个人的事实，因此为所有几何对象提供了相同的隐私保护。但是，某些特定的几何对象可能比其他几何对象对隐私问题更为敏感，因此需要更强的差异性隐私保证。在本文中，我们针对空间数据库引入空间个性化差异隐私的概念，其中不同的几何对象具有不同的隐私保护要求。此外，我们提出了SPDP-PCE，这是一种新颖的空间个性化差分私有机制，用于回答对空间数据库进行距离计数查询的问题，该机制在噪声添加和一致性实施两个步骤中都充分考虑了底层几何空间中几何对象的隐私保护要求。我们在真实数据集上的实验结果证明了SPDP-PCE在各种总隐私预算，查询形状和隐私级别分布下的有效性。一种新颖的空间个性化差异私有机制，用于回答空间数据库上的距离计数查询，该机制在噪声添加和一致性实施两个步骤中都充分考虑了底层几何空间中几何对象的隐私保护要求。我们在真实数据集上的实验结果证明了SPDP-PCE在各种总隐私预算，查询形状和隐私级别分布下的有效性。一种新颖的空间个性化差异私有机制，用于回答空间数据库上的距离计数查询，该机制在噪声添加和一致性实施两个步骤中都充分考虑了底层几何空间中几何对象的隐私保护要求。我们在真实数据集上的实验结果证明了SPDP-PCE在各种总隐私预算，查询形状和隐私级别分布下的有效性。