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Keywords = geomasking

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9 pages, 1242 KB  
Entry
Geomasking to Safeguard Geoprivacy in Geospatial Health Data
by Jue Wang
Encyclopedia 2024, 4(4), 1581-1589; https://doi.org/10.3390/encyclopedia4040103 - 21 Oct 2024
Cited by 1 | Viewed by 5140
Definition
Geomasking is a set of techniques that introduces noise or intentional errors into geospatial data to minimize the risk of identifying exact location information related to individuals while preserving the utility of the data to a controlled extent. It protects the geoprivacy of [...] Read more.
Geomasking is a set of techniques that introduces noise or intentional errors into geospatial data to minimize the risk of identifying exact location information related to individuals while preserving the utility of the data to a controlled extent. It protects the geoprivacy of the data contributor and mitigates potential harm from data breaches while promoting safer data sharing. The development of digital health technologies and the extensive use of individual geospatial data in health studies have raised concerns about geoprivacy. The individual tracking data and health information, if accessed by unauthorized parties, may lead to privacy invasions, criminal activities, and discrimination. These risks underscore the importance of robust protective measures in the collection, management, and sharing of sensitive data. Geomasking techniques have been developed to safeguard geoprivacy in geospatial health data, addressing the risks and challenges associated with data sharing. This entry paper discusses the importance of geoprivacy in geospatial health data and introduces various kinds of geomasking methods and their applications in balancing the protection of individual privacy with the need for data sharing to ensure scientific reproducibility, highlighting the urgent need for more effective geomasking techniques and their applications. Full article
(This article belongs to the Section Mathematics & Computer Science)
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10 pages, 231 KB  
Article
Measurement Errors Arising When Using Distances in Microeconometric Modelling and the Individuals’ Position Is Geo-Masked for Confidentiality
by Giuseppe Arbia, Giuseppe Espa and Diego Giuliani
Econometrics 2015, 3(4), 709-718; https://doi.org/10.3390/econometrics3040709 - 29 Oct 2015
Cited by 10 | Viewed by 6039
Abstract
In many microeconometric models we use distances. For instance, in modelling the individual behavior in labor economics or in health studies, the distance from a relevant point of interest (such as a hospital or a workplace) is often used as a predictor in [...] Read more.
In many microeconometric models we use distances. For instance, in modelling the individual behavior in labor economics or in health studies, the distance from a relevant point of interest (such as a hospital or a workplace) is often used as a predictor in a regression framework. However, in order to preserve confidentiality, spatial micro-data are often geo-masked, thus reducing their quality and dramatically distorting the inferential conclusions. In particular in this case, a measurement error is introduced in the independent variable which negatively affects the properties of the estimators. This paper studies these negative effects, discusses their consequences, and suggests possible interpretations and directions to data producers, end users, and practitioners. Full article
(This article belongs to the Special Issue Spatial Econometrics)
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