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"  This morning, The Guardian released a classified U.S. government document exposing a huge data-gathering operation by the NSA. At the request of the FBI, Verizon Business Network Services was ordered by a secret FISA (Foreign Intelligence Surveillance Act) court to turn over all telephony metadata to the NSA from the period of April 25 to July 19, 2013 (which means it&amp;#039;s presumably ongoing). Let&amp;#039;s start with what this doesn&amp;#039;t mean. No part of this document authorizes the NSA or FBI to listen in on Verizon callers. In fact, the document specifically states that the request does not include the substantive content of the calls. Also, it doesn&amp;#039;t mean that anyone who uses any Verizon services had their calls tracked. Verizon Business Network Services specifically caters to corporate and government clientele (which raises the possibility that the government was spying on itself\u2014not so ironic in the age of aggressive leak investigations).  That said, this document is just one of the FISA requests that has come to light. According to a Department of Justice memo submitted to the Senate in April, the government made 1789 FISA applications for electronic surveillance in 2012\u2014and the court approved 1788. It&amp;#039;s worth mentioning that this is all entirely legal under the Patriot Act. The government is supposed to get a FISA court order for this sort of surveillance, and it apparently did. But why collect such a huge amount of data on Americans&amp;#039; calling records, when surely most of the telephony metadata has nothing to do with terrorism, money laundering, drug trafficking, or anything illegal? To understand why the NSA makes these huge data grabs, it helps to understand the science of data mining, which is why we spoke with Dean Abbott, president of San Diego-based Abbott Analytics. Abbott has no direct involvement in the case, but has been in the data analysis business for 21 years. He talked us through the methodology of predictive analysis, which is how data miners get useful intelligence from large data sets. There are two basic ways to approach the data. First is a supervised learning approach, where you start with a target variable you are trying to predict for (in this case, you could isolate for a high-risk versus non-high-risk communication) and then separate the data by that variable. The second method, which is more useful for this type of request, is unsupervised learning, in which there&amp;#039;s no target variable. You&amp;#039;re simply searching for interesting patterns of behavior that occur in unexpected ways. This type of analysis can be used to create social networks of people of interest.  &amp;quot;You start with an anchor\u2014say, Person A\u2014which is a place to start your network from,&amp;quot; Abbott says. &amp;quot;You see who this Person A has contacted on multiple occasions or at a regular frequency, and that can trigger a connection to another person that is unlikely to be a random connection.&amp;quot; But to get any useful intel this way, Abbott says, you need the whole data set.  As yet, there is no information about exactly what the government was looking for in the Verizon data\u2014it could be anything from terrorism investigations to money laundering to trade sanction violations\u2014but presumably the government ordered Verizon to give up so much data because it didn&amp;#039;t know precisely what or whom they were looking for until they mined the information.  There is a big debate this country needs to have about whether or not the government should be allowed to secretly scour huge pools of data about its citizens&amp;#039; behavior. But so long as U.S. intelligence agencies approach their investigations by attempting to find as many needles in a haystack as they can, they will continue to gather as much hay as possible. "^^<http://www.w3.org/2001/XMLSchema#string>
<http://www.popularmechanics.com/military/a9025/why-the-nsa-wants-all-that-verizon-metadata-15562288/>