Security Analysis for Hilbert Curve Based Spatial Data Privacy-Preserving Method

As cloud computing services and location-aware devices are fully developed, a large amount of data 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 and industry. Although Hilbert curve is widely used in privacy protection for spatial data, the security analysis of standard Hilbert curve (SHC) is seldom proceeded. In this paper, we carefully analyze the characteristics of the points of interest (POI) indexes built by SHC, and visualized the indexes to study the effect of the null value segments. We formally define the null value index to measure the privacy disclosure risk of the space-filling curves (e.g. SHC). An index modification method (SHC*) for SHC is proposed, which can partially violate the distance-preserving property of SHC, so as to obtain better security. The attack model is also defined, in the experiments, the estimated datasets are visualized for explicitly studying, and the estimation distortion shows that SHC* is more secure than SHC.