{"id":145481,"date":"2005-02-01T00:00:00","date_gmt":"2005-02-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/toward-privacy-in-public-databases\/"},"modified":"2018-10-16T20:18:35","modified_gmt":"2018-10-17T03:18:35","slug":"toward-privacy-in-public-databases","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/toward-privacy-in-public-databases\/","title":{"rendered":"Toward Privacy in Public Databases"},"content":{"rendered":"<div class=\"asset-content\">\n<p>We initiate a theoretical study of the census problem. Informally, in a census individual respondents give private information to a trusted party (the census bureau), who publishes a sanitized version of the data. There are two fundamentally con\ufb02icting requirements: privacy for the respondents and utility of the sanitized data. Unlike in the study of secure function evaluation, in which privacy is preserved to the extent possible given a speci\ufb01c functionality goal, in the census problem privacy is paramount; intuitively, things that cannot be learned \u201csafely\u201d should not be learned at all.<\/p>\n<p>An important contribution of this work is a de\ufb01nition of privacy (and privacy compromise) for statistical databases, together with a method for describing and comparing the privacy o\ufb00ered by speci\ufb01c sanitization techniques. We obtain several privacy results using two di\ufb00erent sanitization techniques, and then show how to combine them via cross training. We also obtain two utility results involving clustering.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We initiate a theoretical study of the census problem. Informally, in a census individual respondents give private information to a trusted party (the census bureau), who publishes a sanitized version of the data. There are two fundamentally con\ufb02icting requirements: privacy for the respondents and utility of the sanitized data. Unlike in the study of secure [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13561,13558],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-145481","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-algorithms","msr-research-area-security-privacy-cryptography","msr-locale-en_us"],"msr_publishername":"Springer Verlag","msr_edition":"Second Theory of Cryptography Conference, (TCC 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