True spatial k-anonymity: adaptive areal elimination vs. adaptive areal masking

Charleux, Laure; Schofield, Katherine

doi:10.1080/15230406.2020.1794975

Cited by 10 publications

(5 citation statements)

References 19 publications

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“…Finally, Adaptive Areal Elimination (AAE) (Kounadi & Leitner, 2016), and Adaptive Areal Masking (AAM) (Charleux & Schofield, 2020). Both methods are based on the principle of building anonymization polygons in which there is enough population to mask points.…”

Section: Alternative Geomasking Methodsmentioning

confidence: 99%

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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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Section: Alternative Geomasking Methodsmentioning

confidence: 99%

“…Some geomasking techniques were specifically proposed to mitigate this density heterogeneity, e.g. adaptive areal elimination (Kounadi & Leitner, 2016), and adaptive areal masking (Charleux & Schofield, 2020).…”

Section: Related Workmentioning

confidence: 99%

Geographically Masking Addresses to Study COVID-19 Clusters

Houfaf-Khoufaf

Touya

2021

Preprint

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“…Spatial k-anonymity requires that an individual's geographic location remains indistinguishable from at least k − 1 other locations (Ghinita et al, 2010), ensuring that individual locations in a data set cannot be easily distinguished or linked to specific individuals. One common approach to achieving spatial k-anonymity is to introduce random noise to relocate the original location of an individual within a region containing k − 1 other individual locations (Allshouse et al, 2010;Charleux and Schofield, 2020;Hampton et al, 2010;Hasanzadeh et al, 2020;Kounadi and Leitner, 2016;Seidl et al, 2016;Zhang et al, 2017). Another approach, which is not mentioned in Swanlund and Schuurman (2019), is to perform aggregation by grouping every k individual locations into one (Kounadi and Leitner, 2016;Lin, 2023b;Lin and Xiao, 2023a).…”

Section: Location Privacy and Anonymitymentioning

confidence: 99%

Moving beyond anonymity: Embracing a collective approach to location privacy in data-intensive geospatial analytics

Lin

2024

Environment and Planning F

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Privacy has been an important topic within the geospatial science community, particularly driven by the widespread adoption of geospatial technologies such as mobile devices and the vast amount of location data they generate. This has sparked considerable interest in location privacy that is specifically dedicated to the protection of location information. However, existing literature on location privacy mostly focuses on preserving anonymity and protecting against individual identifiability when using geospatial data. While this is undoubtedly valuable, it may prove insufficient in a landscape characterized by pervasive data collection and analytics. This article argues that the powerful capabilities of algorithms in data-intensive geospatial analytics allow for the profiling of groups of individuals and the prediction of sensitive information that has not been explicitly collected, without necessarily compromising individual identifiability. Nonetheless, these practices pose severe threats to privacy and can contribute to inequality in the treatment of certain populations, leading to structural and societal challenges. In response to these challenges, a collective approach should be embraced to address location privacy concerns. Both regulatory and technical practices need to acknowledge the interdependency of privacy, while individuals should cultivate an awareness of cooperation in privacy protection.

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“…There are two important considerations when applying a geomasking method to a geospatial dataset: data confidentiality and data utility (Armstrong et al., 1999; Charleux & Schofield, 2020; Kounadi & Leitner, 2015; Kwan et al., 2004; Seidl, Paulus, Jankowski, & Regenfelder, 2015; Wang & Kwan, 2020; Zhang, Freundschuh, Lenzer, & Zandbergen, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Previous studies have found that data confidentiality and data utility are affected by the type and parameters of geomasking methods (Armstrong et al., 1999; Kwan et al., 2004). They have also observed a trade‐off relationship between data confidentiality and data utility: geomasking methods that achieve higher data confidentiality would lead to lower data utility and vice versa (Armstrong et al., 1999; Charleux & Schofield, 2020; Kounadi & Leitner, 2015; Kwan et al., 2004; Seidl et al., 2015; Wang & Kwan, 2020; Zhang et al., 2017).…”

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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True spatial k-anonymity: adaptive areal elimination vs. adaptive areal masking

Cited by 10 publications

References 19 publications

Geographically Masking Addresses to Study COVID-19 Clusters

Geographically Masking Addresses to Study COVID-19 Clusters

Moving beyond anonymity: Embracing a collective approach to location privacy in data-intensive geospatial analytics

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

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