{"id":157308,"date":"2009-05-01T00:00:00","date_gmt":"2009-05-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/on-the-complexity-of-differentially-private-data-release-efficient-algorithms-and-hardness-results\/"},"modified":"2018-10-16T21:53:58","modified_gmt":"2018-10-17T04:53:58","slug":"on-the-complexity-of-differentially-private-data-release-efficient-algorithms-and-hardness-results","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/on-the-complexity-of-differentially-private-data-release-efficient-algorithms-and-hardness-results\/","title":{"rendered":"On the Complexity of Differentially Private Data Release: Efficient Algorithms and Hardness Results"},"content":{"rendered":"
\n

We consider private data analysis in the setting in which a trusted and trustworthy curator, having obtained a large data set containing private information, releases to the public a “sanitization” of the data set that simultaneously protects the privacy of the individual contributors of data and offers utility to the data analyst. The sanitization may be in the form of an arbitrary data structure, accompanied by a computational procedure for determining approximate answers to queries on the original data set, or it may be a “synthetic data set” consisting of data items drawn from the same universe as items in the original data set; queries are carried out as if the synthetic data set were the actual input. In either case the process is non-interactive; once the sanitization has been released the original data and the curator play no further role.<\/p>\n

For the task of sanitizing with a synthetic dataset output, we map the boundary between computational feasibility and infeasibility with respect to a variety of utility measures. For the (potentially easier) task of sanitizing with unrestricted output format, we show a tight qualitative and quantitative connection between hardness of sanitizing and the existence of traitor tracing schemes.<\/p>\n<\/div>\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

We consider private data analysis in the setting in which a trusted and trustworthy curator, having obtained a large data set containing private information, releases to the public a “sanitization” of the data set that simultaneously protects the privacy of the individual contributors of data and offers utility to the data analyst. The sanitization may […]<\/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":"Association for Computing Machinery, Inc.","msr_publisher_other":"","msr_booktitle":"Proceedings of the 41th Annual ACM Symposium on Theory of Computing (STOC)","msr_chapter":"","msr_edition":"Proceedings of the 41th Annual ACM Symposium on Theory of Computing (STOC)","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"Copyright \u00a9 2007 by the Association for Computing Machinery, Inc. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and\/or a fee. Request permissions from Publications Dept, ACM Inc., fax +1 (212) 869-0481, or permissions@acm.org. 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