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dc.contributor.authorGarfinkel, Simson L.
dc.contributor.authorAbowd, John M.
dc.contributor.authorMartindale, Christian
dc.date.accessioned2020-12-06T22:17:34Z
dc.date.available2020-12-06T22:17:34Z
dc.date.issued2018-01-01
dc.identifier.other13366728
dc.identifier.urihttps://hdl.handle.net/1813/89104
dc.description.abstractIn 2020 the U.S. Census Bureau will conduct the Constitutionally mandated decennial Census of Population and Housing. Because a census involves collecting large amounts of private data under the promise of confidentiality, traditionally statistics are published only at high levels of aggregation. Published statistical tables are vulnerable to DRAs (database reconstruction attacks), in which the underlying microdata is recovered merely by finding a set of microdata that is consistent with the published statistical tabulations. A DRA can be performed by using the tables to create a set of mathematical constraints and then solving the resulting set of simultaneous equations. This article shows how such an attack can be addressed by adding noise to the published tabulations, so that the reconstruction no longer results in the original data.
dc.language.isoen_US
dc.relation.hasversionPublished in ACMQueue, Vol. 16, No. 5 (September/October 2018): 28-53.
dc.relation.urihttps://queue.acm.org/detail.cfm?id=3295691
dc.subjectStatistics
dc.subjectprivacy
dc.subjectdata
dc.titleUnderstanding Database Reconstruction Attacks on Public Data
dc.description.legacydownloadsGarfinkel_Abowd_Martindale_2018_ACM_QUEUE.pdf: 442 downloads, before Oct. 1, 2020.
local.authorAffiliationGarfinkel, Simson L.: simson.l.garfinkel@census.gov U.S. Census Bureau
local.authorAffiliationAbowd, John M.: john.maron.abowd@census.gov U.S. Census Bureau
local.authorAffiliationMartindale, Christian: U.S. Censue Bureau


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