{"id":145478,"date":"2006-03-01T00:00:00","date_gmt":"2006-03-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/calibrating-noise-to-sensitivity-in-private-data-analysis\/"},"modified":"2018-10-16T22:32:05","modified_gmt":"2018-10-17T05:32:05","slug":"calibrating-noise-to-sensitivity-in-private-data-analysis","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/calibrating-noise-to-sensitivity-in-private-data-analysis\/","title":{"rendered":"Calibrating Noise to Sensitivity in Private Data Analysis"},"content":{"rendered":"

We continue a line of research initiated in [10,11] on privacy-preserving statistical databases. Consider a trusted server that holds a database of sensitive information. Given a query function f<\/em> mapping databases to reals, the so-called true answer<\/em> is the result of applying f<\/em> to the database. To protect privacy, the true answer is perturbed by the addition of random noise generated according to a carefully chosen distribution, and this response, the true answer plus noise, is returned to the user.<\/p>\n

Previous work focused on the case of noisy sums, in which f<\/em> = \u2211 i<\/em> <\/sub> g<\/em>(x<\/em> i<\/em> <\/sub>), where x<\/em> i<\/em> <\/sub> denotes the i<\/em>th row of the database and g<\/em> maps database rows to [0,1]. We extend the study to general functions f<\/em>, proving that privacy can be preserved by calibrating the standard deviation of the noise according to the sensitivity<\/em> of the function f<\/em>. Roughly speaking, this is the amount that any single argument to f<\/em> can change its output. The new analysis shows that for several particular applications substantially less noise is needed than was previously understood to be the case.<\/p>\n

The first step is a very clean characterization of privacy in terms of indistinguishability of transcripts. Additionally, we obtain separation results showing the increased value of interactive sanitization mechanisms over non-interactive.<\/p>\n","protected":false},"excerpt":{"rendered":"

We continue a line of research initiated in [10,11] on privacy-preserving statistical databases. Consider a trusted server that holds a database of sensitive information. Given a query function f mapping databases to reals, the so-called true answer is the result of applying f to the database. To protect privacy, the true answer is perturbed by […]<\/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":[13563,13558],"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-145478","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-data-platform-analytics","msr-research-area-security-privacy-cryptography","msr-locale-en_us"],"msr_publishername":"Springer","msr_edition":"Third Theory of Cryptography Conference (TCC 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