Hilbert-curve based cryptographic transformation scheme for protecting data privacy on outsourced private spatial data

2014 ◽  
Author(s):  
Hyeong-Il Kim ◽  
Seung-Tae Hong ◽  
Jae-Woo Chang
Keyword(s):  
Spatial Data ◽  
Data Privacy ◽  
Hilbert Curve

Tracking Public Participation in Urban Governance

2014 ◽  
pp. 813-830
Author(s):  
Nancy J. Obermeyer
Keyword(s):  
Web 2.0 ◽  
Public Participation ◽  
Spatial Data ◽  
Data Privacy ◽  
Urban Governance ◽  
Data Capture ◽  
Occupy Movement ◽  
History Of ◽  
The Arab Spring ◽  
Gis And Society

This chapter examines the use of GIS, geovisualization, and other geo-locational technologies and applications, including social networking websites and mobile phones associated with Web 2.0, as a tool kit for promoting democratization or leading to loss of data privacy and freedom, focusing on the relevant historical events in 2011 and the first half of 2012. The chapter begins by presenting a brief history of the GIS and society literature, including public participation GIS, volunteered geographic information, and geoslavery. The discussion covers both the rosy view (geospatial and Web 2.0 technologies as a democratizing force) and the gloomy perspective (these same technologies as tools of control based on data capture and loss of privacy). Underlying both of these views are scale and the ability to jump scales, which are examined through the lens of Kevin Cox's (1998) “spaces of dependence and engagement.” Having laid this groundwork, the chapter considers events in the recent past, focusing first on the Arab Spring movements in Tunisia and Egypt and the Occupy movement in the U.S. as examples of the optimistic perspective. It then proceeds to discuss data capture from smart phones and cell phones as examples of the pessimistic view. The chapter concludes with a discussion of how individuals may enhance the democratization potential of geotechnologies and Web 2.0 while minimizing data capture, loss of spatial data privacy, and the harm that these can bring.

scholarly journals Protecting Location Privacy for Outsourced Spatial Data in Cloud Storage

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Feng Tian ◽  
Xiaolin Gui ◽  
Jian An ◽  
Pan Yang ◽  
Jianqiang Zhao ◽  
...  
Keyword(s):  
Spatial Data ◽  
Cloud Storage ◽  
Location Privacy ◽  
Security Analysis ◽  
Transformation Method ◽  
Spatial Transformation ◽  
Hilbert Curve ◽  
Space Filling Curve ◽  
Attack Model ◽  
Transformation Methods

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 theindistinguishabilityand 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.

scholarly journals Multiscale Feature Model for Terrain Data Based on Adaptive Spatial Neighborhood

2013 ◽  
Vol 2013 ◽  
pp. 1-12
Author(s):  
Huijie Zhang ◽  
Yun Ma ◽  
Zhiqiang Ma ◽  
Xinting He ◽  
Yaxin Liu ◽  
...  
Keyword(s):  
Spatial Data ◽  
Large Scale ◽  
Feature Model ◽  
Data Sets ◽  
Scheduling Problems ◽  
Hilbert Curve ◽  
Data Scheduling ◽  
Large Scale Data ◽  
Schedule Time ◽  
Scale Data

Multiresolution hierarchy based on features (FMRH) has been applied in the field of terrain modeling and obtained significant results in real engineering. However, it is difficult to schedule multiresolution data in FMRH from external memory. This paper proposed new multiscale feature model and related strategies to cluster spatial data blocks and solve the scheduling problems of FMRH using spatial neighborhood. In the model, the nodes with similar error in the different layers should be in one cluster. On this basis, a space index algorithm for each cluster guided by Hilbert curve is proposed. It ensures that multi-resolution terrain data can be loaded without traversing the whole FMRH; therefore, the efficiency of data scheduling is improved. Moreover, a spatial closeness theorem of cluster is put forward and is also proved. It guarantees that the union of data blocks composites a whole terrain without any data loss. Finally, experiments have been carried out on many different large scale data sets, and the results demonstrate that the schedule time is shortened and the efficiency of I/O operation is apparently improved, which is important in real engineering.

scholarly journals An improved Hilbert curve for parallel spatial data partitioning

2007 ◽  
Vol 10 (4) ◽  
pp. 282-286 ◽  
Author(s):  
Lingkui Meng ◽  
Changqing Huang ◽  
Chunyu Zhao ◽  
Zhiyong Lin
Keyword(s):  
Spatial Data ◽  
Data Partitioning ◽  
Hilbert Curve

scholarly journals Measuring The Impact of Spatial Perturbations on The Relationship Between Data Privacy and Validity of Descriptive Statistics

2020 ◽  
Author(s):  
Kelly Broen ◽  
Rob Trangucci ◽  
Jon Zelner
Keyword(s):  
Spatial Statistics ◽  
Spatial Patterns ◽  
Spatial Data ◽  
Data Privacy ◽  
Perturbation Methods ◽  
Descriptive Statistics ◽  
Moran’S I ◽  
Mortality Data ◽  
Moran's I ◽  
The Impact

Abstract Background: Like many scientific fields, epidemiology is addressing issues of research reproducibility. Spatial epidemiology, which often uses the inherently identifiable variable of participant address, must balance reproducibility with participant privacy. In this study, we assess the impact of several different data perturbation methods on key spatial statistics and patient privacy. Methods: We analyzed the impact of perturbation on spatial patterns in the full set of address- level mortality data from Lawrence, MA during the period from 1911-1913. The original death locations were perturbed using seven different published approaches to stochastic and deterministic spatial data anonymization. Key spatial descriptive statistics were calculated for each perturbation, including changes in spatial pattern center, Global Moran’s I, Local Moran’s I, distance to the k-th nearest neighbors, and the L-function (a normalized form of Ripley’s K). A spatially adapted form of k-anonymity was used to measure the privacy protection conferred by each method, and the its compliance with HIPAA privacy standards. Results: Random perturbation at 50 meters, donut masking between 5 and 50 meters, and Voronoi masking maintain the validity of descriptive spatial statistics better than other perturbations. Grid center masking with both 100x100 and 250x250 meter cells led to large changes in descriptive spatial statistics. None of the perturbation methods adhered to the HIPAA standard that all points have a k-anonymity > 10. All other perturbation methods employed had at least 265 points, or over 6%, not adhering to the HIPAA standard. Conclusions: Using the set of published perturbation methods applied in this analysis, HIPAA- compliant de-identification was not compatible with maintaining key spatial patterns as measured by our chosen summary statistics. Further research should investigate alternate methods to balancing tradeoffs between spatial data privacy and preservation of key patterns in public health data that are of scientific and medical importance.

scholarly journals Measuring the impact of spatial perturbations on the relationship between data privacy and validity of descriptive statistics

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Kelly Broen ◽  
Rob Trangucci ◽  
Jon Zelner
Keyword(s):  
Spatial Statistics ◽  
Spatial Patterns ◽  
Spatial Data ◽  
Data Privacy ◽  
Perturbation Methods ◽  
Descriptive Statistics ◽  
Moran’S I ◽  
Mortality Data ◽  
Moran's I ◽  
The Impact

Abstract Background Like many scientific fields, epidemiology is addressing issues of research reproducibility. Spatial epidemiology, which often uses the inherently identifiable variable of participant address, must balance reproducibility with participant privacy. In this study, we assess the impact of several different data perturbation methods on key spatial statistics and patient privacy. Methods We analyzed the impact of perturbation on spatial patterns in the full set of address-level mortality data from Lawrence, MA during the period from 1911 to 1913. The original death locations were perturbed using seven different published approaches to stochastic and deterministic spatial data anonymization. Key spatial descriptive statistics were calculated for each perturbation, including changes in spatial pattern center, Global Moran’s I, Local Moran’s I, distance to the k-th nearest neighbors, and the L-function (a normalized form of Ripley’s K). A spatially adapted form of k-anonymity was used to measure the privacy protection conferred by each method, and its compliance with HIPAA and GDPR privacy standards. Results Random perturbation at 50 m, donut masking between 5 and 50 m, and Voronoi masking maintain the validity of descriptive spatial statistics better than other perturbations. Grid center masking with both 100 × 100 and 250 × 250 m cells led to large changes in descriptive spatial statistics. None of the perturbation methods adhered to the HIPAA standard that all points have a k-anonymity > 10. All other perturbation methods employed had at least 265 points, or over 6%, not adhering to the HIPAA standard. Conclusions Using the set of published perturbation methods applied in this analysis, HIPAA and GDPR compliant de-identification was not compatible with maintaining key spatial patterns as measured by our chosen summary statistics. Further research should investigate alternate methods to balancing tradeoffs between spatial data privacy and preservation of key patterns in public health data that are of scientific and medical importance.

Tracking Public Participation in Urban Governance

2013 ◽  
pp. 80-97 ◽  
Author(s):  
Nancy Obermeyer
Keyword(s):  
Web 2.0 ◽  
Public Participation ◽  
Spatial Data ◽  
Data Privacy ◽  
Urban Governance ◽  
Data Capture ◽  
Occupy Movement ◽  
History Of ◽  
The Arab Spring ◽  
Gis And Society

This chapter examines the use of GIS, geovisualization, and other geo-locational technologies and applications, including social networking websites and mobile phones associated with Web 2.0, as a tool kit for promoting democratization or leading to loss of data privacy and freedom, focusing on the relevant historical events in 2011 and the first half of 2012. The chapter begins by presenting a brief history of the GIS and society literature, including public participation GIS, volunteered geographic information, and geoslavery. The discussion covers both the rosy view (geospatial and Web 2.0 technologies as a democratizing force) and the gloomy perspective (these same technologies as tools of control based on data capture and loss of privacy). Underlying both of these views are scale and the ability to jump scales, which are examined through the lens of Kevin Cox’s (1998) “spaces of dependence and engagement.” Having laid this groundwork, the chapter considers events in the recent past, focusing first on the Arab Spring movements in Tunisia and Egypt and the Occupy movement in the U.S. as examples of the optimistic perspective. It then proceeds to discuss data capture from smart phones and cell phones as examples of the pessimistic view. The chapter concludes with a discussion of how individuals may enhance the democratization potential of geotechnologies and Web 2.0 while minimizing data capture, loss of spatial data privacy, and the harm that these can bring.

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