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[ NNSquad ] Poison Attacks Against Machine Learning

Poison Attacks Against Machine Learning

http://j.mp/LGQrRy  (i-programmer)

   "Three researchers, Battista Biggio (Italy) Blaine Nelson and Pavel
    Laskov (Germany), have found a way to feed an SVM with data specially
    designed to increase the error rate of the machine as much as possible
    with a few data points.  The approach assumes that the attacker knows
    the learning algorithm being employed and has access to the same data.
    Less realistically it assumes that the attacker has access to the
    original training data. This is unlikely, but the original training
    data could be approximated by a sample from the population.  With all
    of the data the attacker can manipulate the optimal SVM solution by
    inserting crafted attack points."

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Lauren Weinstein (lauren@vortex.com): http://www.vortex.com/lauren 
Co-Founder: People For Internet Responsibility: http://www.pfir.org 
 - Data Wisdom Explorers League: http://www.dwel.org
 - Network Neutrality Squad: http://www.nnsquad.org 
 - Global Coalition for Transparent Internet Performance: http://www.gctip.org
 - PRIVACY Forum: http://www.vortex.com 
Member: ACM Committee on Computers and Public Policy
Lauren's Blog: http://lauren.vortex.com
Google+: http://vortex.com/g+lauren / Twitter: http://vortex.com/t-lauren 
Tel: +1 (818) 225-2800 / Skype: vortex.com
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