An evaluation of kernel smoothing to protect the confidentiality of individual locations

Lee, Monghyeon; Chun, Yongwan; Griffith, Daniel A.

doi:10.1080/12265934.2018.1482778

Cited by 8 publications

(9 citation statements)

References 24 publications

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“…Geocoding of crime events to a street segment or a city block can encrypt their precise geographic locations [4][5][6]. Transforming a point map to a density map can help mask the exact location of crime events [4,5,11,16,39]. KDE is one of the most commonly used locational protecting methods due to its outstanding ability to obscure location information and at the same time show the spatial distribution patterns of crimes [15,26,40,41].…”

Section: Literature Reviewmentioning

confidence: 99%

“…With the aim of testing the confidentiality of crime locations in density maps, several research works have aimed to recover point maps from density maps [16]. For example, Lee, Chun and Griffith proposed a geometric center-based method to recover point maps [16], while Seidl, et al, evaluated the confidential level of masked maps [15]. Both studies were conducted on the spatial domain, and their recovering accuracies are extremely low for smooth density surfaces.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Compared to the large body of research and applications for density maps, the confidential level of crime locational information in KDE maps has not been systematically investigated [9,11,15,16]. This study aims to examine whether a kernel density map could be reverse-transformed to its original map with discrete crime locations.…”

Section: Introductionmentioning

confidence: 99%

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How Is the Confidentiality of Crime Locations Affected by Parameters in Kernel Density Estimation?

Wang

Liu

Zhou

et al. 2019

IJGI

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Kernel density estimation (KDE) is widely adopted to show the overall crime distribution and at the same time obscure exact crime locations due to the confidentiality of crime data in many countries. However, the confidential level of crime locational information in the KDE map has not been systematically investigated. This study aims to examine whether a kernel density map could be reverse-transformed to its original map with discrete crime locations. Using the Epanecknikov kernel function, a default setting in ArcGIS for density mapping, the transformation from a density map to a point map was conducted with various combinations of parameters to examine its impact on the deconvolution process (density to point location). Results indicate that if the bandwidth parameter (search radius) in the original convolution process (point to density) was known, the original point map could be fully recovered by a deconvolution process. Conversely, when the parameter was unknown, the deconvolution process would be unable to restore the original point map. Experiments on four different point maps—a random point distribution, a simulated monocentric point distribution, a simulated polycentric point distribution, and a real crime location map—show consistent results. Therefore, it can be concluded that the point location of crime events cannot be restored from crime density maps as long as parameters such as the search radius parameter in the density mapping process remain confidential.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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How Is the Confidentiality of Crime Locations Affected by Parameters in Kernel Density Estimation?

Wang

Liu

Zhou

et al. 2019

IJGI

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“…Research involving the confidentiality of locations when publishing smoothed density maps [14,20] shows that it is possible to retrieve the underlying locations whenever the used parameters are published.…”

Section: Introductionmentioning

confidence: 99%

Statistical Disclosure Control When Publishing on Thematic Maps

Hut

Lieshout

Wolf

et al. 2020

Lecture Notes in Computer Science

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The spatial distribution of a variable, such as the energy consumption per company, is usually plotted by colouring regions of the study area according to an underlying table which is already protected from disclosing sensitive information. The result is often heavily influenced by the shape and size of the regions. In this paper, we are interested in producing a continuous plot of the variable directly from microdata and we protect it by adding random noise. We consider a simple attacker scenario and develop an appropriate sensitivity rule that can be used to determine the amount of noise needed to protect the plot from disclosing private information.

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“…This ethical and legal goal is at the expense of data analysis accuracy and precision, as well as data analytic complications such as the ecological fallacy. Meanwhile, depicting finer geographic resolution rates with choropleth or kernel density smoothed maps can maintain patient confidentiality while improving data analysis accuracy and precision (e.g., [ 3 ]), and help avoid or minimize such complications as the ecological fallacy.…”

Section: Introductionmentioning

confidence: 99%

Space-Time Statistical Insights about Geographic Variation in Lung Cancer Incidence Rates: Florida, USA, 2000–2011

Griffith

Chun

2018

IJERPH

Self Cite

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The geographic distribution of lung cancer rates tends to vary across a geographic landscape, and covariates (e.g., smoking rates, demographic factors, socio-economic indicators) commonly are employed in spatial analysis to explain the spatial heterogeneity of these cancer rates. However, such cancer risk factors often are not available, and conventional statistical models are unable to fully capture hidden spatial effects in cancer rates. Introducing random effects in the model specifications can furnish an efficient approach to account for variations that are unexplained due to omitted variables. Especially, a random effects model can be effective for a phenomenon that is static over time. The goal of this paper is to investigate geographic variation in Florida lung cancer incidence data for the time period 2000–2011 using random effects models. In doing so, a Moran eigenvector spatial filtering technique is utilized, which can allow a decomposition of random effects into spatially structured (SSRE) and spatially unstructured (SURE) components. Analysis results confirm that random effects models capture a substantial amount of variation in the cancer data. Furthermore, the results suggest that spatial pattern in the cancer data displays a mixture of positive and negative spatial autocorrelation, although the global map pattern of the random effects term may appear random.

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An evaluation of kernel smoothing to protect the confidentiality of individual locations

Cited by 8 publications

References 24 publications

How Is the Confidentiality of Crime Locations Affected by Parameters in Kernel Density Estimation?

How Is the Confidentiality of Crime Locations Affected by Parameters in Kernel Density Estimation?

Statistical Disclosure Control When Publishing on Thematic Maps

Space-Time Statistical Insights about Geographic Variation in Lung Cancer Incidence Rates: Florida, USA, 2000–2011

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