{"id":159002,"date":"2010-01-01T00:00:00","date_gmt":"2010-01-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/differential-privacy-in-new-settings\/"},"modified":"2018-10-16T21:15:44","modified_gmt":"2018-10-17T04:15:44","slug":"differential-privacy-in-new-settings","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/differential-privacy-in-new-settings\/","title":{"rendered":"Differential Privacy in New Settings"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Differential privacy is a recent notion of privacy tailored to the problem of statistical disclosure control: how to release statistical information about a set of people without compromising the the privacy of any individual.<\/p>\n<p>We describe new work that extends differentially private data analysis beyond the traditional setting of a trusted curator operating, in perfect isolation, on a static dataset. We ask  <\/p>\n<ul>\n<li>How can we guarantee differential privacy, even against an adversary that has access to the algorithm&#8217;s internal state, eg, by subpoena? An algorithm that achives this is said to be pan-private.<\/li>\n<li>How can we guarantee differential privacy when the algorithm must continually produce outputs? We call this differential privacy under continual observation. We also consider these requirements in conjunction.<\/li>\n<\/ul>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Differential privacy is a recent notion of privacy tailored to the problem of statistical disclosure control: how to release statistical information about a set of people without compromising the the privacy of any individual. We describe new work that extends differentially private data analysis beyond the traditional setting of a trusted curator operating, in perfect [&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-159002","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":"Society for Industrial and Applied Mathematics","msr_edition":"Symposium on Discrete 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