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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

Average distance to original point vs. number of anonymization versions. The average distance to original point [km] vs. number of anonymization versions used in averaging is plotted for both Gaussian and uniform skew.
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Figure 2: Average distance to original point vs. number of anonymization versions. The average distance to original point [km] vs. number of anonymization versions used in averaging is plotted for both Gaussian and uniform skew.

Mentions: The average distance to the original address is plotted as a function of the number of separate anonymization passes used in the re-identification inference, for both anonymization methods in Figure 2. Attempts at inferring the original addresses using multiple anonymization passes, show that the average distance inversely varies with the square root of the number of anonymized data sets used in the inference. There is a sharp decrease in the average distance to the original address with 10 anonymization passes and thus a sharp decrease in data set anonymity.


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

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

Average distance to original point vs. number of anonymization versions. The average distance to original point [km] vs. number of anonymization versions used in averaging is plotted for both Gaussian and uniform skew.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Average distance to original point vs. number of anonymization versions. The average distance to original point [km] vs. number of anonymization versions used in averaging is plotted for both Gaussian and uniform skew.
Mentions: The average distance to the original address is plotted as a function of the number of separate anonymization passes used in the re-identification inference, for both anonymization methods in Figure 2. Attempts at inferring the original addresses using multiple anonymization passes, show that the average distance inversely varies with the square root of the number of anonymized data sets used in the inference. There is a sharp decrease in the average distance to the original address with 10 anonymization passes and thus a sharp decrease in data set anonymity.

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