ArticleJournal of exposure science & environmental epidemiology2018
The verified neighbor approach to geoprivacy: An improved method for geographic masking.
Article in Journal of exposure science & environmental epidemiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- MaskMyPy: python tools for performing and analyzing geographic masks.International journal of health geographics · 2025Article
- Article
- Working toward effective anonymization for surveillance data: innovation at South Africa's Agincourt Health and Socio-Demographic Surveillance Site.Population and environment · 2021Article
- Measuring the impact of spatial perturbations on the relationship between data privacy and validity of descriptive statistics.International journal of health geographics · 2021Article
- Street masking: a network-based geographic mask for easily protecting geoprivacy.International journal of health geographics · 2020Article
- Addressing the data guardian and geospatial scientist collaborator dilemma: how to share health records for spatial analysis while maintaining patient confidentiality.International journal of health geographics · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Geographic information adds a powerful component to environmental epidemiology studies but can compromise subject confidentiality. Although locations are often masked by perturbing spatial coordinates, existing masks do not ensure that the perturbation area contains a sufficient number of valid surrogates to prevent disclosure, nor are they designed to minimize perturbation while maintaining a specified level of privacy. I introduce a new approach to geoprivacy in which real property parcel data with information about land use are used to develop a pool of verified neighbors. GIS (geographic information system) processing optionally restricts the pool to residences with values of environmental variables similar to those of the subject parcel. A surrogate is then randomly selected from the k members of the pool closest to the subject with k chosen to achieve the desired spatial privacy protection. The method guarantees the specified level of privacy even where population density is uneven while minimizing spatial distortion and changes to the values of environmental variables assigned to subjects. The method is illustrated with an example that found it to be more effective than random perturbation-based methods in both protecting privacy and preserving spatial fidelity to the original locations.
Indexed as
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.