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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.

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Related in: MedlinePlus

Visualization of POIs' indexes built by SHC, SHC∗, and DSC.
© Copyright Policy - open-access
Related In: Results  -  Collection


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fig7: Visualization of POIs' indexes built by SHC, SHC∗, and DSC.

Mentions: In order to compare the distributions of value segments and continuous segments when employing different spatial transformation methods, we illustrate the POIs' indexes built by SHC, SHC∗, and DSC in Figure 7, where (a.1), (b.1), (c.1), and (d.1) denote the NE, TG, SF, and NA datasets, respectively. And 2, 3, and 4 represent POIs' indexes built by SHC, SHC∗, and DSC, respectively. As described in Section 3.2, the black lines of the histogram represent the continuous segments and the gray parts denote the value segments. We can see that the length of the gray parts decreases dramatically after applying Algorithm 1 to modify the POI's indexes built by SHC. Because SHC∗ compresses value segments, the total length of value segments is far less than SHC, and the distribution of value segments and continuous segments is much more equilibrium than SHC, which makes indistinguishability of SHC∗ much larger than that of SHC, leading to lower privacy disclosure risk. From this figure, we also find out that the distributions of the POIs' indexes built by SHC∗ and DSC are similar, and that is why the indistinguishability of SHC∗ is similar to that of DSC.


Protecting location privacy for outsourced spatial data in cloud storage.

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

Visualization of POIs' indexes built by SHC, SHC∗, and DSC.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig7: Visualization of POIs' indexes built by SHC, SHC∗, and DSC.
Mentions: In order to compare the distributions of value segments and continuous segments when employing different spatial transformation methods, we illustrate the POIs' indexes built by SHC, SHC∗, and DSC in Figure 7, where (a.1), (b.1), (c.1), and (d.1) denote the NE, TG, SF, and NA datasets, respectively. And 2, 3, and 4 represent POIs' indexes built by SHC, SHC∗, and DSC, respectively. As described in Section 3.2, the black lines of the histogram represent the continuous segments and the gray parts denote the value segments. We can see that the length of the gray parts decreases dramatically after applying Algorithm 1 to modify the POI's indexes built by SHC. Because SHC∗ compresses value segments, the total length of value segments is far less than SHC, and the distribution of value segments and continuous segments is much more equilibrium than SHC, which makes indistinguishability of SHC∗ much larger than that of SHC, leading to lower privacy disclosure risk. From this figure, we also find out that the distributions of the POIs' indexes built by SHC∗ and DSC are similar, and that is why the indistinguishability of SHC∗ is similar to that of DSC.

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