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How Will Statistical Agencies Operate When All Data Are Private?

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dc.contributor.authorAbowd, John M.en_US
dc.date.accessioned2016-09-22T19:23:29Z
dc.date.available2016-09-22T19:23:29Z
dc.date.issued2016-09-06
dc.description.abstractThe dual problems of respecting citizen privacy and protecting the confidentiality of their data have become hopelessly conflated in the “Big Data” era. There are orders of magnitude more data outside an agency’s firewall than inside it—compromising the integrity of traditional statistical disclosure limitation methods. And increasingly the information processed by the agency was “asked” in a context wholly outside the agency’s operations—blurring the distinction between what was asked and what is published. Already, private businesses like Microsoft, Google and Apple recognize that cybersecurity (safeguarding the integrity and access controls for internal data) and privacy protection (ensuring that what is published does not reveal too much about any person or business) are two sides of the same coin. This is a paradigm-shifting moment for statistical agencies.en_US
dc.description.legacydownloadsDownloads for this item at https://digitalcommons.ilr.cornell.edu/ldi/30/ as of 9/11/2020: 275
dc.description.sponsorshipNSF Grant 1507241, NSF Grant 1012593 (TC:Large), NSF Grant 1131848 (NCRN), and the Labor Dynamics Institute.en_US
dc.identifier.urihttps://hdl.handle.net/1813/44663
dc.language.isoen_USen_US
dc.publisherJournal of Privacy and Confidentialityen_US
dc.relation.doihttps://doi.org/10.29012/jpc.v7i3.404
dc.subjectprivacyen_US
dc.subjectconfidentiality
dc.subjectdata
dc.titleHow Will Statistical Agencies Operate When All Data Are Private?en_US
dc.typepresentationen_US

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Cornell University NCRN node
Presentations by the Labor Dynamics Institute
Sloan Foundation: The Economics of Socially-Efficient Privacy and Confidentiality Management for Statistical Agencies