{"id":696811,"date":"2020-10-07T17:11:17","date_gmt":"2020-10-08T00:11:17","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=696811"},"modified":"2022-11-16T14:53:04","modified_gmt":"2022-11-16T22:53:04","slug":"mace-a-flexible-framework-for-membership-privacy-estimation-in-generative-models","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/mace-a-flexible-framework-for-membership-privacy-estimation-in-generative-models\/","title":{"rendered":"MACE: A Flexible Framework for Membership Privacy Estimation in Generative Models"},"content":{"rendered":"

Generative machine learning models are being increasingly viewed as a way to share sensitive <\/span>data between institutions. While there has been work on developing differentially private <\/span>generative modeling approaches, these approaches generally lead to sub-par sample quality, <\/span>limiting their use in real world applications. Another line of work has focused on developing <\/span>generative models which lead to higher quality samples but currently lack any formal <\/span>privacy guarantees. In this work, we propose the first formal framework for membership <\/span>privacy estimation in generative models. We formulate the membership privacy risk as a <\/span>statistical divergence between training samples and hold-out samples, and propose sample-<\/span>based methods to estimate this divergence. Compared to previous works, our framework <\/span>makes more realistic and flexible assumptions. First, we offer a generalizable metric as an <\/span>alternative to the accuracy metric (Yeom et al., 2018; Hayes et al., 2019) especially for <\/span>imbalanced datasets. Second, we loosen the assumption of having full access to the underlying <\/span>distribution from previous studies (Yeom et al., 2018; Jayaraman et al., 2020), and propose <\/span>sample-based estimations with theoretical guarantees. Third, along with the population-level <\/span>membership privacy risk estimation via the optimal membership advantage, we offer the <\/span>individual-level estimation via the individual privacy risk. Fourth, our framework allows <\/span>adversaries to access the trained model via a customized query, while prior works require <\/span>specific attributes (Hayes et al., 2019; Chen et al., 2019; Hilprecht et al., 2019).<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

Generative machine learning models are being increasingly viewed as a way to share sensitive data between institutions. While there has been work on developing differentially private generative modeling approaches, these approaches generally lead to sub-par sample quality, limiting their use in real world applications. Another line of work has focused on developing generative models which […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"Transactions on Machine Learning Research (TMLR)","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"","msr_doi":"","msr_arxiv_id":"","msr_s2_paper_id":"","msr_mag_id":"","msr_pubmed_id":"","msr_other_authors":"","msr_other_contributors":"","msr_speaker":"","msr_award":"","msr_affiliation":"","msr_institution":"","msr_host":"","msr_version":"","msr_duration":"","msr_original_fields_of_study":"","msr_release_tracker_id":"","msr_s2_match_type":"","msr_citation_count_updated":"","msr_published_date":"2022-10-6","msr_highlight_text":"","msr_notes":"","msr_longbiography":"","msr_publicationurl":"","msr_external_url":"","msr_secondary_video_url":"","msr_conference_url":"","msr_journal_url":"","msr_s2_pdf_url":"","msr_year":0,"msr_citation_count":0,"msr_influential_citations":0,"msr_reference_count":0,"msr_s2_match_confidence":0,"msr_microsoftintellectualproperty":true,"msr_s2_open_access":false,"msr_s2_author_ids":[],"msr_pub_ids":[],"msr_hide_image_in_river":0,"footnotes":""},"msr-research-highlight":[],"research-area":[13556,13558],"msr-publication-type":[193715],"msr-publisher":[],"msr-focus-area":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-696811","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-research-area-security-privacy-cryptography","msr-locale-en_us"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2022-10-6","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"Transactions on Machine Learning Research (TMLR)","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":1,"msr_main_download":"","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"url","viewUrl":"false","id":"false","title":"https:\/\/openreview.net\/pdf?id=Zxm0kNe3u7","label_id":"243109","label":0}],"msr_related_uploader":"","msr_citation_count":0,"msr_citation_count_updated":"","msr_s2_paper_id":"","msr_influential_citations":0,"msr_reference_count":0,"msr_arxiv_id":"","msr_s2_author_ids":[],"msr_s2_open_access":false,"msr_s2_pdf_url":null,"msr_attachments":[],"msr-author-ordering":[{"type":"user_nicename","value":"Yixi Xu","user_id":39775,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Yixi Xu"},{"type":"user_nicename","value":"Sumit Mukherjee","user_id":39778,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Sumit Mukherjee"},{"type":"text","value":"Xiyang Liu","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Shruti Tople","user_id":39003,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Shruti Tople"},{"type":"user_nicename","value":"Rahul Dodhia","user_id":41401,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Rahul Dodhia"},{"type":"edited_text","value":"Juan M. Lavista Ferres","user_id":39552,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Juan M. Lavista Ferres"}],"msr_impact_theme":[],"msr_research_lab":[199561],"msr_event":[],"msr_group":[559983,696544],"msr_project":[778522,648207],"publication":[],"video":[],"msr-tool":[],"msr_publication_type":"article","related_content":{"projects":[{"ID":778522,"post_title":"AI for Health","post_name":"ai-for-health","post_type":"msr-project","post_date":"2023-05-16 14:26:13","post_modified":"2024-10-14 15:42:21","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/ai-for-health\/","post_excerpt":"AI for Health is a philanthropic program launched by Microsoft, which aims to support nonprofits, researchers, and organizations working on global health challenges. 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