How do people perceive the disclosure risk of maps? Examining the perceived disclosure risk of maps and its implications for geoprivacy protection

Kim, Junghwan; Kwan, Mei‐Po; Levenstein, Margaret C.; Richardson, Douglas S.

doi:10.1080/15230406.2020.1794976

Cited by 25 publications

(15 citation statements)

References 57 publications

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“…That is particularly important for visualization of socially vulnerable people such COVID-19 patients. Kim et al [85] indicated that perceived disclosure risk increases as the amount of locational information displayed on a map increases, and it depends on factors as the map scale and the presence of information of other people. Compared to point-based maps, the perceived disclosure risk is significantly lower for kernel density maps, convex hull maps, and standard deviational ellipse maps.…”

Section: Discussionmentioning

confidence: 99%

Geospatial Analysis and Mapping Strategies for Fine-Grained and Detailed COVID-19 Data with GIS

Carballada

Balsa-Barreiro

2021

IJGI

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The unprecedented COVID-19 pandemic is showing dramatic impact across the world. Public health authorities attempt to fight against the virus while maintaining economic activity. In the face of the uncertainty derived from the virus, all the countries have adopted non-pharmaceutical interventions for limiting the mobility and maintaining social distancing. In order to support these interventions, some health authorities and governments have opted for sharing very fine-grained data related with the impact of the virus in their territories. Geographical science is playing a major role in terms of understanding how the virus spreads across regions. Location of cases allows identifying the spatial patterns traced by the virus. Understanding these patterns makes controlling the virus spread feasible, minimizes its impact in vulnerable regions, anticipates potential outbreaks, or elaborates predictive risk maps. The application of geospatial analysis to fine-grained data must be urgently adopted for optimal decision making in real and near-real time. However, some aspects related to process and map sensitive health data in emergency cases have not yet been sufficiently explored. Among them include concerns about how these datasets with sensitive information must be shown depending on aspects related to data aggregation, scaling, privacy issues, or the need to know in advance the particularities of the study area. In this paper, we introduce our experience in mapping fine-grained data related to the incidence of the COVID-19 during the first wave in the region of Galicia (NW Spain), and after that we discuss the mentioned aspects.

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Section: Discussionmentioning

confidence: 99%

Geospatial Analysis and Mapping Strategies for Fine-Grained and Detailed COVID-19 Data with GIS

Carballada

Balsa-Barreiro

2021

IJGI

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“…Transforming the point information into heat maps is another way to guarantee a good k-anonymity (Z. Wang et al, 2019), and heat maps are perceived as not disclosing privacy by map readers (Kim et al, 2021). Besides k-anonymity, the theoretical advantage of heat maps is the preservation of large clusters that are converted into hot spots in the heat map.…”

Section: Alternative Geomasking Methodsmentioning

confidence: 99%

“…And a recent study shows that visualisation with a high level of detail (e.g. with point symbols for addresses) are perceived as more risky for geoprivacy than heat maps for instance (Kim, Kwan, Levenstein, & Richardson, 2021).…”

Section: Introductionmentioning

confidence: 99%

Geographically Masking Addresses to Study COVID-19 Clusters

Houfaf-Khoufaf

Touya

2021

Preprint

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The spatial analysis of health data usually raises geoprivacy issues. But with the virulence of COVID-19, scientists and crisis managers do need to analyse the spatio-temporal distribution and spreading of the disease with spatially precise data. In particular, it is useful to locate each case on a map to identify clusters of cases in space and time. To allow such analyses with breach of geoprivacy, geomasking techniques are necessary. This paper experiments the geomasking techniques from the literature to solve this problem: masking the real address of positive cases while preserving the local cluster patterns. In particular, two diﬀerent approaches based on aggregation and perturbation are adapted to the geomasking of addresses in areas with diﬀerent densities of population. A new simulated crowding method is also proposed to preserve clusters as much as possible. The results show that geomasking techniques can spatially anonymize addresses while preserving clusters, and the best geomasking method depends on the use of the anonymized data.

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“…In recent years, data sharing has become critical as research reproducibility has received growing attention in the scientific community (Alter & Gonzalez, 2018; Kedron, Li, Fotheringham, & Goodchild, 2021; McNutt, 2014). In the field of geographic information science (GIScience) and public health, however, sharing data can often lead to serious geoprivacy violation because data often contain people's confidential locational information, such as home locations and daily GPS trajectories (Haley et al., 2016; Kim & Kwan, 2021a; Kim, Kwan, Levenstein, & Richardson, 2021; Kounadi & Leitner, 2014; Kwan, Casas, & Schmitz, 2004; Richardson, Kwan, Alter, & McKendry, 2015). To protect people's geoprivacy when sharing data, researchers have developed and applied various geomasking techniques, which deliberately introduce spatial errors to location data to reduce disclosure risks (Armstrong, Rushton, & Zimmerman, 1999; Curtis, Mills, Agustin, & Cocknurn, 2011; Zandbergen, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…A greater distance between the original location and the geomasked location indicates a higher level of data confidentiality. Besides, spatial k‐anonymity is widely used to evaluate spatial data confidentiality and disclosure risk (e.g., Ghinita, Zhao, Papadias, & Kalnis, 2010; Kim et al., 2021; Wang & Kwan, 2020; Zhang et al., 2017). A spatial k‐anonymity index counts the number of all data locations within a buffer region centered at an original location having the distance between the original location and the geomasked location as the radius.…”

Section: Introductionmentioning

confidence: 99%

Travel time errors caused by geomasking might be different between transportation modes and types of urban area

Kim

Kwan

2021

Transactions in GIS

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This article examines the effects of two widely used geomasking methods (aggregation and the bimodal Gaussian method) on errors in car‐ and transit‐based travel times from people's homes to health facilities using Cook County in Illinois as a case study area. It addresses two research questions: (Q1) How do the effects of geomasking on travel time errors differ between transportation modes? (Q2) How do errors in car‐ and transit‐based travel times differ between urban and suburban areas? The results indicate that geomasking introduces considerable errors in travel times. Specifically, errors in transit‐based travel times are significantly higher than those in car‐based travel times. Moreover, when large radii are used for geomasking, errors in car‐based travel times in urban areas are significantly higher than those in suburban areas. On the contrary, transit‐based travel time errors in urban areas are significantly lower than those in suburban areas. Because transportation modes and urban area types play essential roles in travel time errors caused by geomasking, researchers need to mitigate these errors when using geomasked locations for their analysis (e.g., evaluating the spatial accessibility of certain facilities, such as hospitals or healthy food outlets).

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How do people perceive the disclosure risk of maps? Examining the perceived disclosure risk of maps and its implications for geoprivacy protection

Cited by 25 publications

References 57 publications

Geospatial Analysis and Mapping Strategies for Fine-Grained and Detailed COVID-19 Data with GIS

Geospatial Analysis and Mapping Strategies for Fine-Grained and Detailed COVID-19 Data with GIS

Geographically Masking Addresses to Study COVID-19 Clusters

Travel time errors caused by geomasking might be different between transportation modes and types of urban area

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