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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.

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Related in: MedlinePlus

Experimental methods design. One data set of 10,000 artificially generated case locations and unique identifiers were created. The data set was anonymized 50 times using a 2-dimensional Gaussian-based skew, and 50 times using a 2-dimensional uniform skew.
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Figure 6: Experimental methods design. One data set of 10,000 artificially generated case locations and unique identifiers were created. The data set was anonymized 50 times using a 2-dimensional Gaussian-based skew, and 50 times using a 2-dimensional uniform skew.

Mentions: With each subsequent anonymized version, the geocoded points that referred to the same individual address were averaged to estimate the original address. For re-identification inference number n, the anonymized versions of the same address were averaged from data sets [1, n], as shown in Figure 6. For example, the second re-identification inference data set included the averages of addresses from anonymized data sets 1 and 2, the third inference data set included anonymized data from data sets 1, 2, and 3, and so on. After each pass, the distance between the average anonymized point and the original address was calculated.


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

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

Experimental methods design. One data set of 10,000 artificially generated case locations and unique identifiers were created. The data set was anonymized 50 times using a 2-dimensional Gaussian-based skew, and 50 times using a 2-dimensional uniform skew.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 6: Experimental methods design. One data set of 10,000 artificially generated case locations and unique identifiers were created. The data set was anonymized 50 times using a 2-dimensional Gaussian-based skew, and 50 times using a 2-dimensional uniform skew.
Mentions: With each subsequent anonymized version, the geocoded points that referred to the same individual address were averaged to estimate the original address. For re-identification inference number n, the anonymized versions of the same address were averaged from data sets [1, n], as shown in Figure 6. For example, the second re-identification inference data set included the averages of addresses from anonymized data sets 1 and 2, the third inference data set included anonymized data from data sets 1, 2, and 3, and so on. After each pass, the distance between the average anonymized point and the original address was calculated.

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