{"id":24108,"date":"2014-06-21T00:40:57","date_gmt":"2014-06-21T04:40:57","guid":{"rendered":"http:\/\/www.opensource.im\/?p=24108"},"modified":"2014-06-21T00:40:57","modified_gmt":"2014-06-21T04:40:57","slug":"statistical-tricks-extract-sensitive-data-from-encrypted-communications","status":"publish","type":"post","link":"https:\/\/euvolution.com\/open-source-convergence\/encryption\/statistical-tricks-extract-sensitive-data-from-encrypted-communications.php","title":{"rendered":"Statistical Tricks Extract Sensitive Data from Encrypted Communications"},"content":{"rendered":"<p><p>    Research suggests that surveillance agencies could use    statistical tricks to peek through the encryption that protects    Web browsing.  <\/p>\n<p>    Stung by revelations about mass government surveillance,    consumer Web companies are expanding their use of encryption    and releasing more details of those protections to reassure    wary customers. Earlier this year, for instance, Apple released    details of how communications sent via its iMessage service are    encrypted.  <\/p>\n<p>    New research suggests that the U.S. National Security Agency,    or any other organization capable of collecting large    quantities of Web traffic, could extract private information    from encrypted communications by searching for patterns in that    data stream. In tests, analysis of encrypted Internet traffic    could reveal the health conditions a person was researching    online. Similar techniques could glean information about use of    iMessage such as when a person starts typing or what language    they wrote a message in. That research focuses on an approach    known as traffic analysis, which involves using statistical    techniques to find patterns in encrypted communications.  <\/p>\n<p>    Researchers at the University of California, Berkeley, and    Intel developed a particularly effective version targeted    against HTTPS, the form of encryption used to protect websites    and visible to Web surfers as a padlock in a browsers address    bar. The technique involves having software visit the websites    of interest and using machine-learning algorithms to learn the    traffic patterns associated with different pages. Those    patterns are then looked for in a victims traffic trace.  <\/p>\n<p>    The approach proved capable of identifying the pages for    specific medical conditions a person was looking at on the    Planned Parenthood and Mayo Clinic websites even though both    sites encrypt connections with HTTPS. It could also identify    what services a person accessed when he or she logged onto    financial sites including Wells Fargo and Bank of America. On    average, the technique was about 90 percent accurate at    identifying Web pages. A paper on    the Berkeley research will be presented at the Privacy    Enhancing Technologies Symposium in Amsterdam next month.  <\/p>\n<p>    Traffic analysis would be a useful tool for surveillance by    government programs, such as those used by the NSA to collect    and analyze encrypted Internet traffic (see NSA    Leak Leaves Crypto Math Intact but Highlights Known    Workarounds). Corporations with access to Internet traffic    might also have motivation to use it, says Brad Miller, the PhD    candidate at Berkeley who led the research.  <\/p>\n<p>    There are very valid use cases of this type of analysis for    companies, he says. For example, an ISP might want to gain    information about its customers online activity that could be    used to target ads, even if those customers have encrypted    their browsing or communications. Some ISPs, such as Verizon    Wireless, already sell data on their customers browsing to    third parties for such purposes.  <\/p>\n<p>    Scott    Coull, a researcher with the security company RedJack, says the    Berkeley work is the latest in a series of papers showing how    traffic analysis could be used against consumers. When you    look at the worst case for this kind of attack, things dont    look very good, he says.  <\/p>\n<p>    Coull recently found that traffic analysis can be very    effective against messages sent via Apples iMessage, which    are encrypted from the moment they are sent to the moment they    are received. iMessage is by far the worst thing Ive seen,    he says. Coull was able to identify when users started or    stopped typing, were sending or opening a message, the language    a message was written in, and its length, with 96 percent    accuracy or higher.  <\/p>\n<p>    That, combined with the fact that the iMessage protocol    transmits a unique identifier for a device, adds up to similar    metadata to what has been controversially collected by the    NSA on U.S. phone calls, says Coull. If I had the ability to    monitor a big chunk of traffic to and from the iMessage    servers, I could come up with a social network of whom is    messaging whom, and the language theyre using and the    approximate size of the messages, he says.  <\/p>\n<p><!-- Auto Generated --><\/p>\n<p>The rest is here:<br \/>\n<a target=\"_blank\" href=\"http:\/\/www.technologyreview.com\/news\/528336\/statistical-tricks-extract-sensitive-data-from-encrypted-communications\" title=\"Statistical Tricks Extract Sensitive Data from Encrypted Communications\">Statistical Tricks Extract Sensitive Data from Encrypted Communications<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p> Research suggests that surveillance agencies could use statistical tricks to peek through the encryption that protects Web browsing. Stung by revelations about mass government surveillance, consumer Web companies are expanding their use of encryption and releasing more details of those protections to reassure wary customers. Earlier this year, for instance, Apple released details of how communications sent via its iMessage service are encrypted. <\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[45],"tags":[],"class_list":["post-24108","post","type-post","status-publish","format-standard","hentry","category-encryption"],"_links":{"self":[{"href":"https:\/\/euvolution.com\/open-source-convergence\/wp-json\/wp\/v2\/posts\/24108"}],"collection":[{"href":"https:\/\/euvolution.com\/open-source-convergence\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/euvolution.com\/open-source-convergence\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/euvolution.com\/open-source-convergence\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/euvolution.com\/open-source-convergence\/wp-json\/wp\/v2\/comments?post=24108"}],"version-history":[{"count":0,"href":"https:\/\/euvolution.com\/open-source-convergence\/wp-json\/wp\/v2\/posts\/24108\/revisions"}],"wp:attachment":[{"href":"https:\/\/euvolution.com\/open-source-convergence\/wp-json\/wp\/v2\/media?parent=24108"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/euvolution.com\/open-source-convergence\/wp-json\/wp\/v2\/categories?post=24108"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/euvolution.com\/open-source-convergence\/wp-json\/wp\/v2\/tags?post=24108"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}