{"id":622779,"date":"2014-08-20T10:00:03","date_gmt":"2014-08-20T17:00:03","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=622779"},"modified":"2019-11-22T10:56:26","modified_gmt":"2019-11-22T18:56:26","slug":"xray-increasing-the-webs-transparency-with-differential-correlation","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/xray-increasing-the-webs-transparency-with-differential-correlation\/","title":{"rendered":"XRay: Increasing the Web’s Transparency with Differential Correlation"},"content":{"rendered":"

Today\u2019s Web services\u2014such as Google, Amazon, and Facebook\u2014leverage user data for varied purposes, including personalizing recommendations, targeting advertisements, and adjusting prices. At present, users have little insight into how their data is being used. Hence, they cannot make informed choices about the services they choose. To increase transparency, we developed XRay<\/em>, the \ufb01rst \ufb01ne-grained, robust, and scalable personal data tracking system for the Web. XRay<\/em> predicts which data in an arbitrary Web account (such as emails, searches, or viewed products) is being used to target which outputs (such as ads, recommended products, or prices). XRay<\/em>\u2019s core functions are service agnostic and easy to instantiate for new services, and they can track data within and across services. To make predictions independent of the audited service, XRay<\/em> relies on the following insight: by comparing outputs from different accounts with similar, but not identical, subsets of data, one can pinpoint targeting through correlation. We show both theoretically, and through experiments on Gmail, Amazon, and YouTube, that XRay<\/em> achieves high precision and recall by correlating data from a surprisingly small number of extra accounts.<\/p>\n","protected":false},"excerpt":{"rendered":"

Today\u2019s Web services\u2014such as Google, Amazon, and Facebook\u2014leverage user data for varied purposes, including personalizing recommendations, targeting advertisements, and adjusting prices. At present, users have little insight into how their data is being used. Hence, they cannot make informed choices about the services they choose. To increase transparency, we developed XRay, the \ufb01rst \ufb01ne-grained, robust, […]<\/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":"","msr_number":"","msr_organization":"ACSA","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"USENIX 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