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February 2018

Provably Secure Machine Learning

February 26, 2018 @ 4:00 pm - 5:00 pm

Jacob Steinhardt (Stanford)

32-G449 (Kiva/Patel)

Abstract:  The widespread use of machine learning systems creates a new class of computer security vulnerabilities where, rather than attacking the integrity of the software itself, malicious actors exploit the statistical nature of the learning algorithms. For instance, attackers can add fake data (e.g. by creating fake user accounts), or strategically manipulate inputs to the system once it is deployed. So far, attempts to defend against these attacks have focused on empirical performance against known sets of attacks. I will argue that…

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