Data-Mining for Terrorists Not ‘Feasible’
Along with the article “Name Matching in Law Enforcement and Counter-Terrorism,” there have been several posts on this blog about terror watch lists (see Michael L.’s post and Karen’s post) and cases of mistaken identity (Kentaro’s cell phone blacklist adventure). I just read this article, from Wired Magazine’s Threat Level Blog, about a recent report entitled “Protecting Individual Privacy in the Struggle Against Terrorists,” which criticizes the US Department of Homeland Security’s attempt at identifying terrorist activity by datamining every possible database of personal information, from phone records to credit card transactions. The article states:
“Automated identification of terrorists through data mining (or any other known methodology) is neither feasible as an objective nor desirable as a goal of technology development efforts,” the report found. “Even in well-managed programs, such tools are likely to return significant rates of false positives, especially if the tools are highly automated.”
I am very glad Wired put up a blog post about this, because the actual report is over 350 pages long. The executive summary of the report mentions that one huge problem with this type of data mining is, of course, that criminals can circumvent being listed in databases by (a) not participating in them, and (b) using false identities if they do.
Michael Lissner Said,
October 8, 2008 @ 1:27 pm
I can’t wait until we get a more realistic set of goals for our security personnel. Talk about government waste. Sadly though, what politician can possibly call for less security? Sigh.
Shawna Hein Said,
October 13, 2008 @ 9:37 am
i wonder how much of this is just old-school tactics combined with excitement about the ease of data collection. In other words, security was used to having “data-mining” strategies before the age of terrabytes of personal data online. Now that all this personal data is there, everyone’s like “MUST COLLECT THIS.” and “MUST TRUST MACHINES.” Without clear reasoning about strategy and false matches.