Privacy-preserving approach for outsourced spatial data based on POI distribution

With the popularity of cloud computing services and location-aware devices, a large amount of information related to location needs to be outsourced to the service provider, so the research about privacy protection for spatial data gets increasing attention from academia. As a kind of spatial transformation approach, Hilbert curve is widely used in privacy protection for spatial data. However, the standard Hilbert curve does not take the distribution of the points of interest (POI) into consideration, so the curve parameters may need to be adjusted several times. Moreover, it cannot support custom authorization of the space for the data owner. To solve these problems, in this paper, first, we propose the adaptive Hilbert curve (AHC), which dynamically adapts to the POI distribution. AHC partitions the space into atom regions according to the setting capacity, and then determines the order of atom region based on the Hilbert curve fractal rule. The transformation key tree is constructed based on the atom region order, and the data owner can share part of the key tree with the authorized user, so as to realize the custom authorization of the space. Second, a spatial query processing scheme based on AHC is designed, including the index value calculation algorithm for POI, range and KNN query processing schemes. Third, the null value index is defined to quantify the leakage risk of privacy information. Finally, a series of experiments are conducted using real and synthetic datasets, and the results show that, in the aspect of spatial transformation, AHC is more security and provides higher query efficiency than the standard Hilbert curve.