Limits...
Protecting location privacy for outsourced spatial data in cloud storage.

Tian F, Gui X, An J, Yang P, Zhao J, Zhang X - ScientificWorldJournal (2014)

Bottom Line: As cloud computing services and location-aware devices are fully developed, a large amount of spatial data needs to be outsourced to the cloud storage provider, so the research on privacy protection for outsourced spatial data gets increasing attention from academia and industry.But sufficient security analysis for standard Hilbert curve (SHC) is seldom proceeded.The evaluation results indicate that SHC(∗) and DSC are more secure than SHC, and DSC achieves the best index generation performance.

View Article: PubMed Central - PubMed

Affiliation: School of Electronics and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China ; Shaanxi Province Key Laboratory of Computer Network, Xi'an Jiaotong University, Xi'an 710049, China.

ABSTRACT
As cloud computing services and location-aware devices are fully developed, a large amount of spatial data needs to be outsourced to the cloud storage provider, so the research on privacy protection for outsourced spatial data gets increasing attention from academia and industry. As a kind of spatial transformation method, Hilbert curve is widely used to protect the location privacy for spatial data. But sufficient security analysis for standard Hilbert curve (SHC) is seldom proceeded. In this paper, we propose an index modification method for SHC (SHC(∗)) and a density-based space filling curve (DSC) to improve the security of SHC; they can partially violate the distance-preserving property of SHC, so as to achieve better security. We formally define the indistinguishability and attack model for measuring the privacy disclosure risk of spatial transformation methods. The evaluation results indicate that SHC(∗) and DSC are more secure than SHC, and DSC achieves the best index generation performance.

Show MeSH

Related in: MedlinePlus

Index generation algorithm for DSC (IGD).
© Copyright Policy - open-access
Related In: Results  -  Collection


getmorefigures.php?uid=PMC4109601&req=5

alg2: Index generation algorithm for DSC (IGD).

Mentions: Index Generation for DSC. After partitioning spatial domain and generating quad tree nodes, it is necessary to generate the leaf nodes' DSC value and intermediate nodes' subcurve orientation and starting point according to the preset curve orientation and starting point of DSC, shown in Algorithm 2. Meanwhile, the index value of each POI is set the same as the DSC value of the partitioned region that the POI belongs to. This algorithm employs Hilbert curve fractal rules shown in Figure 4.


Protecting location privacy for outsourced spatial data in cloud storage.

Tian F, Gui X, An J, Yang P, Zhao J, Zhang X - ScientificWorldJournal (2014)

Index generation algorithm for DSC (IGD).
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4109601&req=5

alg2: Index generation algorithm for DSC (IGD).
Mentions: Index Generation for DSC. After partitioning spatial domain and generating quad tree nodes, it is necessary to generate the leaf nodes' DSC value and intermediate nodes' subcurve orientation and starting point according to the preset curve orientation and starting point of DSC, shown in Algorithm 2. Meanwhile, the index value of each POI is set the same as the DSC value of the partitioned region that the POI belongs to. This algorithm employs Hilbert curve fractal rules shown in Figure 4.

Bottom Line: As cloud computing services and location-aware devices are fully developed, a large amount of spatial data needs to be outsourced to the cloud storage provider, so the research on privacy protection for outsourced spatial data gets increasing attention from academia and industry.But sufficient security analysis for standard Hilbert curve (SHC) is seldom proceeded.The evaluation results indicate that SHC(∗) and DSC are more secure than SHC, and DSC achieves the best index generation performance.

View Article: PubMed Central - PubMed

Affiliation: School of Electronics and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China ; Shaanxi Province Key Laboratory of Computer Network, Xi'an Jiaotong University, Xi'an 710049, China.

ABSTRACT
As cloud computing services and location-aware devices are fully developed, a large amount of spatial data needs to be outsourced to the cloud storage provider, so the research on privacy protection for outsourced spatial data gets increasing attention from academia and industry. As a kind of spatial transformation method, Hilbert curve is widely used to protect the location privacy for spatial data. But sufficient security analysis for standard Hilbert curve (SHC) is seldom proceeded. In this paper, we propose an index modification method for SHC (SHC(∗)) and a density-based space filling curve (DSC) to improve the security of SHC; they can partially violate the distance-preserving property of SHC, so as to achieve better security. We formally define the indistinguishability and attack model for measuring the privacy disclosure risk of spatial transformation methods. The evaluation results indicate that SHC(∗) and DSC are more secure than SHC, and DSC achieves the best index generation performance.

Show MeSH
Related in: MedlinePlus