{"id":641136,"date":"2020-03-04T19:03:01","date_gmt":"2020-03-05T03:03:01","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=641136"},"modified":"2021-06-09T18:42:42","modified_gmt":"2021-06-10T01:42:42","slug":"locally-private-hypothesis-selection","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/locally-private-hypothesis-selection\/","title":{"rendered":"Locally Private Hypothesis Selection"},"content":{"rendered":"

We initiate the study of hypothesis selection under local differential privacy. Given samples from an unknown probability distribution\u00a0p<\/span><\/span><\/span><\/span>\u00a0and a set of\u00a0k<\/span><\/span><\/span><\/span>\u00a0probability distributions\u00a0Q<\/span><\/span><\/span><\/span><\/span><\/span>, we aim to output, under the constraints of\u00a0\u03b5<\/span><\/span><\/span><\/span>-local differential privacy, a distribution from\u00a0Q<\/span><\/span><\/span><\/span><\/span><\/span>\u00a0whose total variation distance to\u00a0p<\/span><\/span><\/span><\/span>\u00a0is comparable to the best such distribution.<\/p>\n","protected":false},"excerpt":{"rendered":"

We initiate the study of hypothesis selection under local differential privacy. Given samples from an unknown probability distribution\u00a0p\u00a0and a set of\u00a0k\u00a0probability distributions\u00a0Q, we aim to output, under the constraints of\u00a0\u03b5-local differential privacy, a distribution from\u00a0Q\u00a0whose total variation distance to\u00a0p\u00a0is comparable to the best such distribution.<\/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":[13561,13546,13558],"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-641136","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-algorithms","msr-research-area-computational-sciences-mathematics","msr-research-area-security-privacy-cryptography","msr-locale-en_us"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2020-7-1","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":0,"msr_main_download":"","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"url","viewUrl":"false","id":"false","title":"https:\/\/arxiv.org\/abs\/2002.09465","label_id":"243109","label":0}],"msr_related_uploader":"","msr_attachments":[],"msr-author-ordering":[{"type":"user_nicename","value":"Sivakanth Gopi","user_id":37830,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Sivakanth Gopi"},{"type":"text","value":"Gautam Kamath","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Janardhan Kulkarni","user_id":32147,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Janardhan Kulkarni"},{"type":"text","value":"Aleksandar Nikolov","user_id":0,"rest_url":false},{"type":"text","value":"Zhiwei Steven Wu","user_id":0,"rest_url":false},{"type":"text","value":"Huanyu 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