Proceedings of the 1st ACM SIGSPATIAL International Workshop on Privacy in Geographic Information Collection and Analysis 2014
DOI: 10.1145/2675682.2676399
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Protecting patient geo-privacy via a triangular displacement geo-masking method

Abstract: Protecting patient geo-privacy while allowing for valid geographic analyses of the data is a major challenge [1]. As a consequence, a variety of methods have been introduced to mask patients' locational information, also called geo-masking methods [2]. This study assessed the five main geo-masking methods as cited by [3] in terms of re-identification risk and performance. These five methods are Random Direction and Fixed Radius, Random Perturbation within a Circle, Gaussian Displacement, Donut Masking, and Bim… Show more

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Cited by 8 publications
(7 citation statements)
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References 23 publications
(37 reference statements)
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“…Exchanges the mobility traces of two different individuals for a certain period of time. Geographic coordinates these are presented in [15], where density is had under consideration in the methods. in [2] is described displacement, change of scale, rotation.…”
Section: Swappingmentioning
confidence: 99%
“…Exchanges the mobility traces of two different individuals for a certain period of time. Geographic coordinates these are presented in [15], where density is had under consideration in the methods. in [2] is described displacement, change of scale, rotation.…”
Section: Swappingmentioning
confidence: 99%
“…Still, an open question remains whether the value k should be a function of the density of the population or the underlying road network to protect the confidentiality of sensitive locations even in areas where there are small variations in the routes. A similar issue has been discussed in many geographical masking studies that have suggested weighting the displacement distance by population density because in less densely populated areas the risk of re-identification is higher (e.g., Armstrong and Ruggles, 2005;Kwan et al, 2004;Murad et al, 2014;Seidl et al, 2015).…”
Section: Discussionmentioning
confidence: 71%
“…Geo-masking (Seidl et al 2016, Zhang et al 2017 Adaptive geo-masking, Voronoibased aggregation system (Croft et al 2016) Triangular displacement (Murad et al 2014) k-anonymity (Samarati andSweeney 1998, Sweeney 2002). Point aggrigation and adaptive arial elimination.…”
Section: Spatial Cloakingmentioning
confidence: 99%