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dc.contributor.authorCassa, Christopher Anthony
dc.contributor.authorWieland, Shannon C.
dc.contributor.authorMandl, Kenneth David
dc.date.accessioned2013-01-08T16:52:50Z
dc.date.issued2008
dc.identifier.citationCassa, Christopher A., Shannon C. Wieland, and Kenneth D. Mandl. 2008. Re-identification of home addresses from spatial locations anonymized by Gaussian skew. International Journal of Health Geographics 7:45.en_US
dc.identifier.issn1476-072Xen_US
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:10140311
dc.description.abstractBackground: 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 reidentify 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 nondeterministic 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.en_US
dc.language.isoen_USen_US
dc.publisherBioMed Centralen_US
dc.relation.isversionofdoi://10.1186/1476-072X-7-45en_US
dc.relation.hasversionhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC2526988/pdf/en_US
dc.relation.hasversionhttp://www.ij-healthgeographics.com/content/7/1/45en_US
dash.licenseLAA
dc.titleRe-Identification of Home Addresses from Spatial Locations Anonymized by Gaussian Skewen_US
dc.typeJournal Articleen_US
dc.description.versionVersion of Recorden_US
dc.relation.journalInternational Journal of Health Geographicsen_US
dash.depositing.authorMandl, Kenneth David
dc.date.available2013-01-08T16:52:50Z
dash.affiliation.otherHMS^Pediatrics-Children's Hospitalen_US
dash.affiliation.otherHMS^Health Sciences and Technologyen_US
dash.affiliation.otherHMS^Pediatrics-Children's Hospitalen_US
dc.identifier.doi10.1186/1476-072X-7-45*
dash.contributor.affiliatedCassa, Christopher
dash.contributor.affiliatedMandl, Kenneth


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