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Ensuring Confidentiality of Geocoded Health Data: Assessing Geographic Masking Strategies for Individual-Level Data.

Zandbergen PA - Adv Med (2014)

Bottom Line: One commonly used technique to protect confidentiality when releasing individual-level geocoded data is geographic masking.This typically consists of applying a certain amount of random perturbation in a systematic manner to reduce the risk of reidentification.Despite recent progress, no universally accepted or endorsed geographic masking technique has emerged.

View Article: PubMed Central - PubMed

Affiliation: Department of Geography, University of New Mexico, Albuquerque, NM 87131, USA.

ABSTRACT
Public health datasets increasingly use geographic identifiers such as an individual's address. Geocoding these addresses often provides new insights since it becomes possible to examine spatial patterns and associations. Address information is typically considered confidential and is therefore not released or shared with others. Publishing maps with the locations of individuals, however, may also breach confidentiality since addresses and associated identities can be discovered through reverse geocoding. One commonly used technique to protect confidentiality when releasing individual-level geocoded data is geographic masking. This typically consists of applying a certain amount of random perturbation in a systematic manner to reduce the risk of reidentification. A number of geographic masking techniques have been developed as well as methods to quantity the risk of reidentification associated with a particular masking method. This paper presents a review of the current state-of-the-art in geographic masking, summarizing the various methods and their strengths and weaknesses. Despite recent progress, no universally accepted or endorsed geographic masking technique has emerged. Researchers on the other hand are publishing maps using geographic masking of confidential locations. Any researcher publishing such maps is advised to become familiar with the different masking techniques available and their associated reidentification risks.

No MeSH data available.


Related in: MedlinePlus

Example of geographic masking technique (i.e., random placement within a circle) using an additional spatial filter to constrain displacement. The red dot represents the original location; the yellow area represents all possible locations for the masked location; and the blue dot represents one possible masked location selected randomly. This filter can be used to avoid placement in areas where logically no population resides (such as water bodies or parks) or to limit displacement to a particular enumeration unit (such as the same census tract or postal code).
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fig6: Example of geographic masking technique (i.e., random placement within a circle) using an additional spatial filter to constrain displacement. The red dot represents the original location; the yellow area represents all possible locations for the masked location; and the blue dot represents one possible masked location selected randomly. This filter can be used to avoid placement in areas where logically no population resides (such as water bodies or parks) or to limit displacement to a particular enumeration unit (such as the same census tract or postal code).

Mentions: One variation on geographic masking is the use of additional spatial filters to ensure masked locations fall within predefined areas of interest. For example, displacement could be limited to a physical land base by excluding surface water bodies (e.g., oceans, bays, rivers, and lakes) to ensure that no masked locations appear in areas which are obviously uninhabited. Another potential use of such filters is to ensure masked locations stay within the same enumeration units (e.g., census block group, postal code) as the original location. The use of such additional spatial filters is illustrated in Figure 6.


Ensuring Confidentiality of Geocoded Health Data: Assessing Geographic Masking Strategies for Individual-Level Data.

Zandbergen PA - Adv Med (2014)

Example of geographic masking technique (i.e., random placement within a circle) using an additional spatial filter to constrain displacement. The red dot represents the original location; the yellow area represents all possible locations for the masked location; and the blue dot represents one possible masked location selected randomly. This filter can be used to avoid placement in areas where logically no population resides (such as water bodies or parks) or to limit displacement to a particular enumeration unit (such as the same census tract or postal code).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4590956&req=5

fig6: Example of geographic masking technique (i.e., random placement within a circle) using an additional spatial filter to constrain displacement. The red dot represents the original location; the yellow area represents all possible locations for the masked location; and the blue dot represents one possible masked location selected randomly. This filter can be used to avoid placement in areas where logically no population resides (such as water bodies or parks) or to limit displacement to a particular enumeration unit (such as the same census tract or postal code).
Mentions: One variation on geographic masking is the use of additional spatial filters to ensure masked locations fall within predefined areas of interest. For example, displacement could be limited to a physical land base by excluding surface water bodies (e.g., oceans, bays, rivers, and lakes) to ensure that no masked locations appear in areas which are obviously uninhabited. Another potential use of such filters is to ensure masked locations stay within the same enumeration units (e.g., census block group, postal code) as the original location. The use of such additional spatial filters is illustrated in Figure 6.

Bottom Line: One commonly used technique to protect confidentiality when releasing individual-level geocoded data is geographic masking.This typically consists of applying a certain amount of random perturbation in a systematic manner to reduce the risk of reidentification.Despite recent progress, no universally accepted or endorsed geographic masking technique has emerged.

View Article: PubMed Central - PubMed

Affiliation: Department of Geography, University of New Mexico, Albuquerque, NM 87131, USA.

ABSTRACT
Public health datasets increasingly use geographic identifiers such as an individual's address. Geocoding these addresses often provides new insights since it becomes possible to examine spatial patterns and associations. Address information is typically considered confidential and is therefore not released or shared with others. Publishing maps with the locations of individuals, however, may also breach confidentiality since addresses and associated identities can be discovered through reverse geocoding. One commonly used technique to protect confidentiality when releasing individual-level geocoded data is geographic masking. This typically consists of applying a certain amount of random perturbation in a systematic manner to reduce the risk of reidentification. A number of geographic masking techniques have been developed as well as methods to quantity the risk of reidentification associated with a particular masking method. This paper presents a review of the current state-of-the-art in geographic masking, summarizing the various methods and their strengths and weaknesses. Despite recent progress, no universally accepted or endorsed geographic masking technique has emerged. Researchers on the other hand are publishing maps using geographic masking of confidential locations. Any researcher publishing such maps is advised to become familiar with the different masking techniques available and their associated reidentification risks.

No MeSH data available.


Related in: MedlinePlus