Limits...
Re-identification of home addresses from spatial locations anonymized by Gaussian skew.

Cassa CA, Wieland SC, Mandl KD - Int J Health Geogr (2008)

Bottom Line: We produce multiple anonymized data sets using a single set of addresses and then progressively average the anonymized results related to each address, characterizing the steep decline in distance from the re-identified point to the original location, (and the reduction in privacy).With ten anonymized copies of an original data set, we find a substantial decrease in average distance from 0.7 km to 0.2 km between the estimated, re-identified address and the original address.With fifty anonymized copies of an original data set, we find a decrease in average distance from 0.7 km to 0.1 km.

View Article: PubMed Central - HTML - PubMed

Affiliation: Children's Hospital Informatics Program, Children's Hospital Boston, Boston, MA, USA. cassa@mit.edu

ABSTRACT

Background: Knowledge of the geographical locations of individuals is fundamental to the practice of spatial epidemiology. One approach to preserving the privacy of individual-level addresses in a data set is to de-identify the data using a non-deterministic blurring algorithm that shifts the geocoded values. We investigate a vulnerability in this approach which enables an adversary to re-identify individuals using multiple anonymized versions of the original data set. If several such versions are available, each can be used to incrementally refine estimates of the original geocoded location.

Results: We produce multiple anonymized data sets using a single set of addresses and then progressively average the anonymized results related to each address, characterizing the steep decline in distance from the re-identified point to the original location, (and the reduction in privacy). With ten anonymized copies of an original data set, we find a substantial decrease in average distance from 0.7 km to 0.2 km between the estimated, re-identified address and the original address. With fifty anonymized copies of an original data set, we find a decrease in average distance from 0.7 km to 0.1 km.

Conclusion: We demonstrate that multiple versions of the same data, each anonymized by non-deterministic Gaussian skew, can be used to ascertain original geographic locations. We explore solutions to this problem that include infrastructure to support the safe disclosure of anonymized medical data to prevent inference or re-identification of original address data, and the use of a Markov-process based algorithm to mitigate this risk.

Show MeSH

Related in: MedlinePlus

Anonymization algorithm translation probability density functions. Probability distribution functions for the two anonymization methods, 2-dimensional Gaussian skew (left) and uniform skew (right).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2526988&req=5

Figure 5: Anonymization algorithm translation probability density functions. Probability distribution functions for the two anonymization methods, 2-dimensional Gaussian skew (left) and uniform skew (right).

Mentions: A data set containing artificially-generated geocoded values for 10,000 sample patients was created using a spatial cluster creation tool [14,15]. All points were uniformly distributed within a circle of radius 800 m centered in Boston, MA, and assigned a unique numeric identifier for tracking. Each of the geocoded addresses was then anonymized using a Gaussian 2-dimensional spatial blur skew that was adjusted for population density [5], fifty separate times. A second anonymization approach, a uniform skew, was used to create a second group of 50 anonymized data sets. Each geocode that was anonymized using the uniform skew method was moved a distance, in meters, ranging from [-λ, λ], independently in each dimension. Figure 5 describes the 2-dimensional probability distribution function for both of these anonymization algorithms.


Re-identification of home addresses from spatial locations anonymized by Gaussian skew.

Cassa CA, Wieland SC, Mandl KD - Int J Health Geogr (2008)

Anonymization algorithm translation probability density functions. Probability distribution functions for the two anonymization methods, 2-dimensional Gaussian skew (left) and uniform skew (right).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2526988&req=5

Figure 5: Anonymization algorithm translation probability density functions. Probability distribution functions for the two anonymization methods, 2-dimensional Gaussian skew (left) and uniform skew (right).
Mentions: A data set containing artificially-generated geocoded values for 10,000 sample patients was created using a spatial cluster creation tool [14,15]. All points were uniformly distributed within a circle of radius 800 m centered in Boston, MA, and assigned a unique numeric identifier for tracking. Each of the geocoded addresses was then anonymized using a Gaussian 2-dimensional spatial blur skew that was adjusted for population density [5], fifty separate times. A second anonymization approach, a uniform skew, was used to create a second group of 50 anonymized data sets. Each geocode that was anonymized using the uniform skew method was moved a distance, in meters, ranging from [-λ, λ], independently in each dimension. Figure 5 describes the 2-dimensional probability distribution function for both of these anonymization algorithms.

Bottom Line: We produce multiple anonymized data sets using a single set of addresses and then progressively average the anonymized results related to each address, characterizing the steep decline in distance from the re-identified point to the original location, (and the reduction in privacy).With ten anonymized copies of an original data set, we find a substantial decrease in average distance from 0.7 km to 0.2 km between the estimated, re-identified address and the original address.With fifty anonymized copies of an original data set, we find a decrease in average distance from 0.7 km to 0.1 km.

View Article: PubMed Central - HTML - PubMed

Affiliation: Children's Hospital Informatics Program, Children's Hospital Boston, Boston, MA, USA. cassa@mit.edu

ABSTRACT

Background: Knowledge of the geographical locations of individuals is fundamental to the practice of spatial epidemiology. One approach to preserving the privacy of individual-level addresses in a data set is to de-identify the data using a non-deterministic blurring algorithm that shifts the geocoded values. We investigate a vulnerability in this approach which enables an adversary to re-identify individuals using multiple anonymized versions of the original data set. If several such versions are available, each can be used to incrementally refine estimates of the original geocoded location.

Results: We produce multiple anonymized data sets using a single set of addresses and then progressively average the anonymized results related to each address, characterizing the steep decline in distance from the re-identified point to the original location, (and the reduction in privacy). With ten anonymized copies of an original data set, we find a substantial decrease in average distance from 0.7 km to 0.2 km between the estimated, re-identified address and the original address. With fifty anonymized copies of an original data set, we find a decrease in average distance from 0.7 km to 0.1 km.

Conclusion: We demonstrate that multiple versions of the same data, each anonymized by non-deterministic Gaussian skew, can be used to ascertain original geographic locations. We explore solutions to this problem that include infrastructure to support the safe disclosure of anonymized medical data to prevent inference or re-identification of original address data, and the use of a Markov-process based algorithm to mitigate this risk.

Show MeSH
Related in: MedlinePlus