MaskMy.XYZ: An easy‐to‐use tool for protecting geoprivacy using geographic masks

Swanlund, David; Schuurman, Nadine; Brussoni, Mariana

doi:10.1111/tgis.12606

Cited by 14 publications

(20 citation statements)

References 23 publications

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“…2b. Random perturbation, firstly proposed by Armstrong et al [20], is one of the most widely used geomasking methods [47]. The method displaces each point in a dataset to a random location within a buffer area centered at the original point [20].…”

Section: Daily Activity Locations (Dal) K-anonymity For Individual Gpmentioning

confidence: 99%

Daily activity locations k-anonymity for the evaluation of disclosure risk of individual GPS datasets

Wang

Kwan

2020

Int J Health Geogr

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Background: Personal privacy is a significant concern in the era of big data. In the field of health geography, personal health data are collected with geographic location information which may increase disclosure risk and threaten personal geoprivacy. Geomasking is used to protect individuals' geoprivacy by masking the geographic location information, and spatial k-anonymity is widely used to measure the disclosure risk after geomasking is applied. With the emergence of individual GPS trajectory datasets that contains large volumes of confidential geospatial information, disclosure risk can no longer be comprehensively assessed by the spatial k-anonymity method. Methods: This study proposes and develops daily activity locations (DAL) k-anonymity as a new method for evaluating the disclosure risk of GPS data. Instead of calculating disclosure risk based on only one geographic location (e.g., home) of an individual, the new DAL k-anonymity is a composite evaluation of disclosure risk based on all activity locations of an individual and the time he/she spends at each location abstracted from GPS datasets. With a simulated individual GPS dataset, we present case studies of applying DAL k-anonymity in various scenarios to investigate its performance. The results of applying DAL k-anonymity are also compared with those obtained with spatial k-anonymity under these scenarios. Results: The results of this study indicate that DAL k-anonymity provides a better estimation of the disclosure risk than does spatial k-anonymity. In various case-study scenarios of individual GPS data, DAL k-anonymity provides a more effective method for evaluating the disclosure risk by considering the probability of re-identifying an individual's home and all the other daily activity locations. Conclusions: This new method provides a quantitative means for understanding the disclosure risk of sharing or publishing GPS data. It also helps shed new light on the development of new geomasking methods for GPS datasets. Ultimately, the findings of this study will help to protect individual geoprivacy while benefiting the research community by promoting and facilitating geospatial data sharing.

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Section: Daily Activity Locations (Dal) K-anonymity For Individual Gpmentioning

confidence: 99%

Daily activity locations k-anonymity for the evaluation of disclosure risk of individual GPS datasets

Wang

Kwan

2020

Int J Health Geogr

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“…Data custodians can utilize the travel time information to exclude locations that can result in high travel time errors. In this light, an interactive geomasking platform between data custodians and data users for addressing travel time errors may be helpful (e.g., Ajayakumar, Curtis, & Curtis, 2019; Swanlund et al., 2020).…”

Section: Resultsmentioning

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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“…Point data, even when only disclosed on published maps, can often be reverse engineered (Curtis et al 2006, Kounadi and Leitner 2014). To alleviate this, geomasking (or perturbing) techniques can be applied to such data (Swanlund et al 2020) but LBS provide additional challenges to privacy as they can contain more precise, expansive, and linkable data than conventional point data sets (Gao et al 2019). De-anonymization and re-identification are very serious concerns.…”

Section: Privacy and Ethicsmentioning

confidence: 99%

Identifying home locations in human mobility data: an open-source R package for comparison and reproducibility

Chen

Poorthuis

2021

International Journal of Geographical Information Science

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Identifying meaningful locations, such as home or work, from human mobility data has become an increasingly common prerequisite for geographic research. Although location-based services (LBS) and other mobile technology have rapidly grown in recent years, it can be challenging to infer meaningful places from such data, whichcompared to conventional datasetscan be devoid of context. Existing approaches are often developed ad-hoc and can lack transparency and reproducibility. To address this, we introduce an R software package for inferring home locations from LBS data. The package implements pre-existing algorithms and provides building blocks to make writing algorithmic 'recipes' more convenient. We evaluate this approach by analyzing a de-identified LBS dataset from Singapore that aims to balance ethics and privacy with the research goal of identifying meaningful locations. We show that ensemble approaches, combining multiple algorithms, can be especially valuable in this regard as the resulting patterns of inferred home locations closely correlate with the distribution of residential population. We hope this package, and others like it, will contribute to an increase in use and sharing of comparable algorithms, research code and data. This will increase transparency

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MaskMy.XYZ: An easy‐to‐use tool for protecting geoprivacy using geographic masks

Cited by 14 publications

References 23 publications

Daily activity locations k-anonymity for the evaluation of disclosure risk of individual GPS datasets

Daily activity locations k-anonymity for the evaluation of disclosure risk of individual GPS datasets

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

Identifying home locations in human mobility data: an open-source R package for comparison and reproducibility

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